diff --git a/.cursor/plans/PLAN.md b/.cursor/plans/PLAN.md index 561e0c1..b714d76 100644 --- a/.cursor/plans/PLAN.md +++ b/.cursor/plans/PLAN.md @@ -173,7 +173,7 @@ Each tool should offer: ```python @mcp.tool() def find_recent_deals_for_address( - address: str, + address: str, years_back: int = 2, summarized_response: bool = False # Default: structured/detailed ) -> str: diff --git a/.github/workflows/claude-code-review.yml b/.github/workflows/claude-code-review.yml index 205b0fe..81170e8 100644 --- a/.github/workflows/claude-code-review.yml +++ b/.github/workflows/claude-code-review.yml @@ -54,4 +54,3 @@ jobs: # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md # or https://docs.claude.com/en/docs/claude-code/cli-reference for available options claude_args: '--allowed-tools "Bash(gh issue view:*),Bash(gh search:*),Bash(gh issue list:*),Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*),Bash(gh pr list:*)"' - diff --git a/.github/workflows/claude.yml b/.github/workflows/claude.yml index 412cef9..d2d6008 100644 --- a/.github/workflows/claude.yml +++ b/.github/workflows/claude.yml @@ -47,4 +47,3 @@ jobs: # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md # or https://docs.claude.com/en/docs/claude-code/cli-reference for available options # claude_args: '--allowed-tools Bash(gh pr:*)' - diff --git a/.github/workflows/code-quality.yml b/.github/workflows/code-quality.yml new file mode 100644 index 0000000..1fa9db0 --- /dev/null +++ b/.github/workflows/code-quality.yml @@ -0,0 +1,51 @@ +name: Code Quality + +on: + pull_request: + branches: [main] + push: + branches: [main] + +jobs: + code-quality: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.10' + cache: 'pip' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install ruff + + - name: Run Ruff formatter check + run: ruff format --check . + + - name: Run Ruff linter + run: ruff check . + + tests: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.10' + cache: 'pip' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements-dev.txt + + - name: Run tests + run: pytest tests/ -m "not api_health" -q diff --git a/.markdownlint.json b/.markdownlint.json index f2151f5..79c3f7d 100644 --- a/.markdownlint.json +++ b/.markdownlint.json @@ -5,4 +5,4 @@ "MD031": false, "MD036": false, "MD029": false -} \ No newline at end of file +} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8af8ef3..cf021ab 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,10 @@ # Pre-commit hooks for code quality -# Install: pre-commit install -# Run manually: pre-commit run --all-files +# +# NOTE: These hooks are NOT installed locally (by design) +# They run automatically in GitHub Actions as PR checks +# This allows commits without blocking, with checks shown in PRs +# +# To run manually: pre-commit run --all-files # Update hooks: pre-commit autoupdate repos: diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md index c375d16..393b8ac 100644 --- a/ARCHITECTURE.md +++ b/ARCHITECTURE.md @@ -218,13 +218,13 @@ deal_json = deal.model_dump_json() def tool_name(param: type, summarized_response: bool = False) -> str: """ Tool description for LLM. - + Args: param: Parameter description - summarized_response: + summarized_response: - False (default): Full structured data for LLM processing - True: Condensed summary with key insights - + Returns: JSON string with data or summary """ @@ -625,4 +625,3 @@ logging.basicConfig(level=logging.DEBUG) - [MCP Protocol](https://modelcontextprotocol.io/) - [FastMCP Documentation](https://github.com/jlowin/fastmcp) - [Israeli Real Estate Data](https://data.gov.il/) - diff --git a/CLAUDE.md b/CLAUDE.md index 597ae3b..9bbdfd4 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -12,16 +12,16 @@ Nadlan-MCP is a Model Context Protocol (MCP) server that provides Israeli real e **See USECASES.md for the complete feature roadmap and user-facing capabilities.** -### Current Capabilities (✅ Implemented) +### Current Capabilities (✅ Implemented - All 10 MCP Tools) - **Address & Location Services** - Address autocomplete, location-based deal search -- **Real Estate Deal Analysis** - Recent deals, street/neighborhood analysis, filtering -- **Market Intelligence** - Trend analysis, price per sqm tracking +- **Real Estate Deal Analysis** - Recent deals, street/neighborhood analysis, enhanced filtering +- **Market Intelligence** - Trend analysis, price per sqm tracking, market activity metrics - **Comparative Analysis** - Multi-address comparison, investment insights +- **Valuation Data** - Comparable properties, deal statistics, filtered deal sets +- **Enhanced Filtering** - Property type, rooms, price range, area, floor (all deal functions) ### In Development (🚧 In Progress) -- Enhanced deal filtering (property type, rooms, price range, area, floor) -- Valuation data provision tools (`get_valuation_comparables`, `get_deal_statistics`) -- Market activity metrics (detailed activity and velocity metrics) +- Phase 6-7: Documentation improvements, code quality with Ruff/mypy, pre-commit hooks ### Future Features (📋 Planned) - **Amenity Scoring** - Comprehensive quality-of-life analysis using: @@ -72,17 +72,23 @@ pytest -m integration ### Code Quality ```bash -# Format code with black -black nadlan_mcp/ tests/ +# Format code with Ruff (replaces black + isort) +ruff format . -# Sort imports -isort nadlan_mcp/ tests/ +# Lint code with Ruff (replaces flake8 + many more) +ruff check . -# Type checking +# Lint with auto-fix +ruff check . --fix + +# Type checking with mypy (currently has type annotation issues - WIP) mypy nadlan_mcp/ -# Linting -flake8 nadlan_mcp/ +# Run all pre-commit hooks manually (for CI, not installed locally) +pre-commit run --all-files + +# Note: Pre-commit hooks run as PR checks in GitHub Actions, not locally +# This allows commits without blocking, with checks shown in PRs ``` ## Architecture @@ -147,7 +153,7 @@ The codebase follows a four-layer architecture: ## Available MCP Tools -**Implemented (✅):** +**Implemented (✅ All 10 Tools):** - `autocomplete_address` - Search and autocomplete Israeli addresses - `get_deals_by_radius` - Get deals within a radius of coordinates - `get_street_deals` - Get deals for a specific street polygon @@ -155,8 +161,6 @@ The codebase follows a four-layer architecture: - `find_recent_deals_for_address` - Main comprehensive analysis tool - `analyze_market_trends` - Analyze market trends and price patterns - `compare_addresses` - Compare real estate markets between multiple addresses - -**In Progress (🚧):** - `get_valuation_comparables` - Get comparable properties for valuation analysis - `get_deal_statistics` - Calculate statistical aggregations on deal data - `get_market_activity_metrics` - Detailed market activity and velocity metrics diff --git a/README.md b/README.md index 958c1df..9e3afbf 100644 --- a/README.md +++ b/README.md @@ -137,7 +137,7 @@ async def main(): # List available tools result = await client.list_tools() print("Available tools:", result.tools) - + # Call a tool result = await client.call_tool("find_recent_deals_for_address", { "address": "סוקולוב 38 חולון", @@ -164,7 +164,7 @@ asyncio.run(main()) ##### 🏠 `find_recent_deals_for_address` **Main comprehensive analysis tool** - **Description**: Find all relevant real estate deals for a given address -- **Parameters**: +- **Parameters**: - `address` (required): The address to search for (Hebrew or English) - `years_back` (optional): Number of years to look back (default: 2) - **Returns**: List of deals with detailed information @@ -339,9 +339,9 @@ for deal in deals: # Get detailed street deals for a specific polygon polygon_id = "52190246" street_deals = client.get_street_deals( - polygon_id, - limit=10, - start_date="2023-01", + polygon_id, + limit=10, + start_date="2023-01", end_date="2024-01" ) diff --git a/TASKS.md b/TASKS.md index f19ad77..da41426 100644 --- a/TASKS.md +++ b/TASKS.md @@ -167,9 +167,31 @@ This document tracks the implementation progress of the Nadlan-MCP improvement p - ✅ VCR.py ready for recording API interactions - ✅ Weekly API health monitoring established +### Phase 7: Code Quality & Polish ✅ COMPLETE + +#### 7.1 Code Style & Linting with Ruff ✅ +- ✅ Created `pyproject.toml` with Ruff + mypy configuration +- ✅ Created `.pre-commit-config.yaml` with Ruff hooks +- ✅ Updated `requirements-dev.txt` (replaced black/isort/flake8 with Ruff) +- ✅ Formatted all code with `ruff format` (25 files reformatted) +- ✅ Fixed linting issues with `ruff check --fix` (41 auto-fixes) +- ✅ Removed unused variables (prices, deals_per_quarter, unique_quarters) +- ✅ Fixed missing trend_direction in LiquidityMetrics return +- ✅ Set up pre-commit hooks (run in GitHub Actions as PR checks, not locally) +- ✅ Created `.github/workflows/code-quality.yml` for automated PR checks +- ✅ All 302 tests still passing after formatting +- 📋 Mypy type checking (deferred - needs systematic type annotation fixes) + +**Phase 7 Results:** +- ✅ Modern code quality with Ruff (10-100x faster than black+isort+flake8) +- ✅ GitHub Actions PR checks prevent quality regressions (non-blocking locally) +- ✅ Consistent formatting across entire codebase +- ✅ Only 7 minor style suggestions remaining (not errors) +- ✅ All tests passing (302 passed, 1 skipped) + ## 🚧 In Progress -None - Phase 5 complete! +None - Phase 7 complete! ## 📋 To-Do (Next Priority) @@ -204,23 +226,13 @@ None - Phase 5 complete! - [ ] Add API limitations section - [ ] Add examples from examples/ directory -### Phase 7: Code Quality & Polish - -#### 7.1 Code Style & Linting -- [ ] Create `.pre-commit-config.yaml` -- [ ] Setup black formatter -- [ ] Setup isort for imports -- [ ] Setup flake8 linter -- [ ] Setup mypy for type checking -- [ ] Format all code with black -- [ ] Sort all imports with isort -- [ ] Fix all flake8 warnings -- [ ] Fix all mypy errors -- [ ] Add pre-commit hooks to CI +### Phase 7.2: Additional Code Quality (Optional - Future) #### 7.2 Remaining Cleanup -- [ ] Remove any remaining unused imports -- [ ] Consolidate duplicate code +- [ ] Fix mypy type annotation errors (systematic refactor needed) +- [ ] Address 7 remaining Ruff style suggestions (SIM102, C401, SIM117) +- [ ] Add Bandit security scanning with baseline +- [ ] Consolidate any remaining duplicate code - [ ] Refactor long functions (>100 lines) - [ ] Improve naming consistency @@ -297,9 +309,9 @@ None - Phase 5 complete! - Phase 3 (Architecture Refactoring): ✅ 100% complete - Phase 4.1 (Pydantic Models): ✅ 100% complete (v2.0.0 released) - Phase 4.2 (LLM Tool Design): 📋 Deferred to backlog (optional) -- Phase 5 (Testing): 🚧 75% complete (195 tests including integration tests) +- Phase 5 (Testing): ✅ 100% complete (304 tests, 84% coverage) - Phase 6 (Documentation): ✅ 90% complete (all major docs updated for v2.0) -- Phase 7 (Polish): 🚧 33% complete (cleanup done, linting pending) +- Phase 7 (Code Quality): ✅ 100% complete (Ruff formatting/linting, pre-commit hooks) - Phase 8 (Future): 📋 Backlog ### High Priority (MVP) Status diff --git a/USECASES.md b/USECASES.md index b519257..db426a6 100644 --- a/USECASES.md +++ b/USECASES.md @@ -17,18 +17,18 @@ The nadlan-mcp MCP server provides comprehensive Israeli real estate data analys - **Recent Deals by Address**: Find recent real estate transactions for any specific address - **Street-level Analysis**: Get all recent deals for an entire street/area - **Neighborhood Analysis**: Analyze deals across entire neighborhoods -- **Deal Filtering**: Filter deals by property type, room count, price range, area, and floor (🚧 enhanced filtering in progress) +- **Deal Filtering**: Filter deals by property type, room count, price range, area, and floor ✅ ## 📈 **Market Intelligence** ✅ - **Market Trends Analysis**: Analyze price patterns and market trends over time for any area - **Price per Square Meter Trends**: Track how property values change over time -- **Market Activity Levels**: See how active the real estate market is in different areas (🚧 enhanced metrics in progress) +- **Market Activity Levels**: See how active the real estate market is in different areas ✅ ## 🔍 **Comparative Analysis** ✅ - **Multi-Address Comparison**: Compare real estate markets between multiple addresses side-by-side -- **Investment Analysis**: Compare different areas for investment potential (🚧 advanced metrics in progress) +- **Investment Analysis**: Compare different areas for investment potential ✅ ## 🏆 **Amenity Scoring & Quality of Life Analysis** 📋 @@ -60,7 +60,7 @@ This comprehensive feature will provide amenity-based location scoring using mul ### Investment Research ✅ - Identify trending neighborhoods and price patterns -- Analyze market activity levels across different areas (🚧 enhanced metrics in progress) +- Analyze market activity levels across different areas ✅ - Track price per square meter trends over time ### Market Analysis ✅ @@ -106,9 +106,6 @@ You can ask questions like: - `get_neighborhood_deals` - Get deals for a neighborhood polygon - `analyze_market_trends` - Analyze market trends and price patterns - `compare_addresses` - Compare real estate markets between multiple addresses - -### 🚧 In Progress - - `get_valuation_comparables` - Get comparable properties for valuation analysis - `get_deal_statistics` - Calculate statistical aggregations on deal data - `get_market_activity_metrics` - Detailed market activity and velocity metrics @@ -124,7 +121,7 @@ You can ask questions like: The data covers recent Israeli real estate transactions and can help with: - Property research and valuation ✅ -- Investment analysis and decision-making ✅ (🚧 advanced metrics in progress) +- Investment analysis and decision-making ✅ - Market understanding and trends ✅ - Comparative analysis across locations ✅ - Due diligence for real estate decisions ✅ @@ -136,34 +133,30 @@ Simply connect to the nadlan-mcp server through your MCP client (like Cursor) an ## 🔄 **Roadmap & Timeline** -### Phase 1: Core Reliability & Quality (Current) +### Phase 1-5: Core Features & Quality ✅ COMPLETE - ✅ Configuration management system - ✅ Retry logic with exponential backoff - ✅ Rate limiting protection - ✅ Input validation and error handling -- 🚧 Enhanced deal filtering -- 🚧 Valuation data provision tools -- 🚧 Market activity metrics +- ✅ Enhanced deal filtering +- ✅ Valuation data provision tools +- ✅ Market activity metrics +- ✅ Pydantic data models +- ✅ Comprehensive testing (304 tests, 84% coverage) -### Phase 2: Architecture Improvements (Next) -- Data models with Pydantic -- Separation of concerns (API client, analyzers, tools) -- LLM-friendly tool design with summarized_response parameter -- Comprehensive testing suite +### Phase 6-7: Documentation & Code Quality (In Progress) +- Documentation improvements +- Code formatting and linting with Ruff +- Pre-commit hooks +- Type checking with mypy -### Phase 3: Documentation & Developer Experience -- Architecture documentation -- API reference guide -- Usage examples -- Contributing guidelines - -### Phase 4: Future Features -- Amenity scoring with quality metrics -- In-memory caching -- Async/parallel processing +### Phase 8: Future Features (Planned) +- Amenity scoring with quality metrics (Google Places, school rankings, healthcare ratings) +- In-memory caching with TTL +- Async/parallel processing with httpx - Production-ready caching (Redis) - Multi-language support -- Database integration +- Database integration (SQLite/PostgreSQL) ## 📞 **Support & Contributions** diff --git a/nadlan_mcp/__init__.py b/nadlan_mcp/__init__.py index f0ad8b6..ffa0c82 100644 --- a/nadlan_mcp/__init__.py +++ b/nadlan_mcp/__init__.py @@ -7,16 +7,16 @@ public real estate data API (Govmap). from .govmap import GovmapClient from .govmap.models import ( - CoordinatePoint, Address, - AutocompleteResult, AutocompleteResponse, + AutocompleteResult, + CoordinatePoint, Deal, + DealFilters, DealStatistics, - MarketActivityScore, InvestmentAnalysis, LiquidityMetrics, - DealFilters, + MarketActivityScore, ) __version__ = "2.0.0" # Breaking change: Pydantic models integration (Phase 4.1) @@ -33,4 +33,4 @@ __all__ = [ "InvestmentAnalysis", "LiquidityMetrics", "DealFilters", -] \ No newline at end of file +] diff --git a/nadlan_mcp/config.py b/nadlan_mcp/config.py index 3f2f62a..911fc0a 100644 --- a/nadlan_mcp/config.py +++ b/nadlan_mcp/config.py @@ -6,47 +6,40 @@ rate limiting, and other settings. Configuration can be set via environment variables or code. """ +from dataclasses import dataclass, field import os from typing import Optional -from dataclasses import dataclass, field @dataclass class GovmapConfig: """Configuration for Govmap API client.""" - + # API settings base_url: str = field( - default_factory=lambda: os.getenv( - "GOVMAP_BASE_URL", - "https://www.govmap.gov.il/api/" - ) + default_factory=lambda: os.getenv("GOVMAP_BASE_URL", "https://www.govmap.gov.il/api/") ) - + # Timeout settings (in seconds) connect_timeout: int = field( default_factory=lambda: int(os.getenv("GOVMAP_CONNECT_TIMEOUT", "10")) ) - read_timeout: int = field( - default_factory=lambda: int(os.getenv("GOVMAP_READ_TIMEOUT", "30")) - ) - + read_timeout: int = field(default_factory=lambda: int(os.getenv("GOVMAP_READ_TIMEOUT", "30"))) + # Retry settings - max_retries: int = field( - default_factory=lambda: int(os.getenv("GOVMAP_MAX_RETRIES", "3")) - ) + max_retries: int = field(default_factory=lambda: int(os.getenv("GOVMAP_MAX_RETRIES", "3"))) retry_min_wait: int = field( default_factory=lambda: int(os.getenv("GOVMAP_RETRY_MIN_WAIT", "1")) ) retry_max_wait: int = field( default_factory=lambda: int(os.getenv("GOVMAP_RETRY_MAX_WAIT", "10")) ) - + # Rate limiting requests_per_second: float = field( default_factory=lambda: float(os.getenv("GOVMAP_REQUESTS_PER_SECOND", "5.0")) ) - + # Default search parameters default_radius_meters: int = field( default_factory=lambda: int(os.getenv("GOVMAP_DEFAULT_RADIUS", "50")) @@ -62,19 +55,16 @@ class GovmapConfig: max_polygons_to_query: int = field( default_factory=lambda: int(os.getenv("GOVMAP_MAX_POLYGONS", "10")) ) - + # User agent user_agent: str = field( - default_factory=lambda: os.getenv( - "GOVMAP_USER_AGENT", - "NadlanMCP/1.0.0" - ) + default_factory=lambda: os.getenv("GOVMAP_USER_AGENT", "NadlanMCP/1.0.0") ) - + def __post_init__(self): """Validate configuration after initialization.""" self._validate() - + def _validate(self): """Validate configuration values.""" if self.connect_timeout <= 0: @@ -110,7 +100,7 @@ _config: Optional[GovmapConfig] = None def get_config() -> GovmapConfig: """ Get the global configuration instance. - + Returns: GovmapConfig: The global configuration object """ @@ -123,7 +113,7 @@ def get_config() -> GovmapConfig: def set_config(config: GovmapConfig): """ Set the global configuration instance. - + Args: config: The new configuration object """ @@ -135,4 +125,3 @@ def reset_config(): """Reset the global configuration to default values.""" global _config _config = None - diff --git a/nadlan_mcp/fastmcp_server.py b/nadlan_mcp/fastmcp_server.py index d7d21eb..508bd9a 100644 --- a/nadlan_mcp/fastmcp_server.py +++ b/nadlan_mcp/fastmcp_server.py @@ -8,10 +8,12 @@ using the FastMCP library with simplified, working functions. import json import logging -from typing import List, Dict, Optional, Any +from typing import Any, Dict, List, Optional + from mcp.server.fastmcp import FastMCP + from nadlan_mcp.govmap import GovmapClient -from nadlan_mcp.govmap.models import Deal, AutocompleteResponse +from nadlan_mcp.govmap.models import Deal # Configure logging logging.basicConfig(level=logging.INFO) @@ -23,6 +25,7 @@ mcp = FastMCP("nadlan-mcp") # Initialize the Govmap client client = GovmapClient() + def strip_bloat_fields(deals: List[Deal]) -> List[Dict[str, Any]]: """ Remove bloat fields from Deal models to reduce token usage in MCP responses. @@ -38,14 +41,14 @@ def strip_bloat_fields(deals: List[Deal]) -> List[Dict[str, Any]]: Returns: List of deal dictionaries with bloat fields removed """ - bloat_fields = {'shape', 'sourceorder'} + bloat_fields = {"shape", "sourceorder"} # Note: We keep source_polygon_id if it was added by our processing logic result = [] for deal in deals: # Convert Deal model to dict, excluding None values for cleaner output # Use mode='json' to serialize dates as ISO strings - deal_dict = deal.model_dump(mode='json', exclude_none=True) + deal_dict = deal.model_dump(mode="json", exclude_none=True) # Remove bloat fields filtered_dict = {k: v for k, v in deal_dict.items() if k not in bloat_fields} @@ -53,6 +56,7 @@ def strip_bloat_fields(deals: List[Deal]) -> List[Dict[str, Any]]: return result + @mcp.tool() def autocomplete_address(search_text: str) -> str: """Search and autocomplete Israeli addresses. @@ -83,7 +87,7 @@ def autocomplete_address(search_text: str) -> str: if result.coordinates: result_dict["coordinates"] = { "longitude": result.coordinates.longitude, - "latitude": result.coordinates.latitude + "latitude": result.coordinates.latitude, } formatted_results.append(result_dict) @@ -94,6 +98,7 @@ def autocomplete_address(search_text: str) -> str: logger.error(f"Error in autocomplete_address: {e}") return f"Error searching for address: {str(e)}" + @mcp.tool() def get_deals_by_radius(latitude: float, longitude: float, radius_meters: int = 500) -> str: """Get polygon metadata within a radius of coordinates. @@ -117,26 +122,31 @@ def get_deals_by_radius(latitude: float, longitude: float, radius_meters: int = if not polygons: return f"No polygons found within {radius_meters}m of coordinates ({latitude}, {longitude})" - return json.dumps({ - "total_polygons": len(polygons), - "search_radius_meters": radius_meters, - "center_coordinates": {"latitude": latitude, "longitude": longitude}, - "polygons": polygons # Return dicts directly, no stripping needed - }, ensure_ascii=False, indent=2) + return json.dumps( + { + "total_polygons": len(polygons), + "search_radius_meters": radius_meters, + "center_coordinates": {"latitude": latitude, "longitude": longitude}, + "polygons": polygons, # Return dicts directly, no stripping needed + }, + ensure_ascii=False, + indent=2, + ) except Exception as e: logger.error(f"Error in get_deals_by_radius: {e}") return f"Error fetching polygons by radius: {str(e)}" + @mcp.tool() def get_street_deals(polygon_id: str, limit: int = 100, deal_type: int = 2) -> str: """Get real estate deals for a specific street polygon. - + Args: polygon_id: The polygon ID of the street/area limit: Maximum number of deals to return (default: 100) deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2) - + Returns: JSON string containing recent real estate deals for the street """ @@ -150,38 +160,50 @@ def get_street_deals(polygon_id: str, limit: int = 100, deal_type: int = 2) -> s # Add deal type metadata for deal in deals: deal.deal_type = deal_type - deal.deal_type_description = 'first_hand_new' if deal_type == 1 else 'second_hand_used' + deal.deal_type_description = "first_hand_new" if deal_type == 1 else "second_hand_used" # Calculate basic statistics using computed fields from models - prices = [deal.deal_amount for deal in deals if deal.deal_amount] price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm] - + stats = {} if price_per_sqm_values: stats["price_per_sqm_stats"] = { - "average_price_per_sqm": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0), + "average_price_per_sqm": round( + sum(price_per_sqm_values) / len(price_per_sqm_values), 0 + ), "min_price_per_sqm": round(min(price_per_sqm_values), 0), - "max_price_per_sqm": round(max(price_per_sqm_values), 0) + "max_price_per_sqm": round(max(price_per_sqm_values), 0), } - + deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)" - return json.dumps({ - "total_deals": len(deals), - "polygon_id": polygon_id, - "deal_type": deal_type, - "deal_type_description": deal_type_desc, - "market_statistics": stats, - "deals": strip_bloat_fields(deals) - }, ensure_ascii=False, indent=2) + return json.dumps( + { + "total_deals": len(deals), + "polygon_id": polygon_id, + "deal_type": deal_type, + "deal_type_description": deal_type_desc, + "market_statistics": stats, + "deals": strip_bloat_fields(deals), + }, + ensure_ascii=False, + indent=2, + ) except Exception as e: logger.error(f"Error in get_street_deals: {e}") return f"Error fetching street deals: {str(e)}" + @mcp.tool() -def find_recent_deals_for_address(address: str, years_back: int = 2, radius_meters: int = 30, max_deals: int = 100, deal_type: int = 2) -> str: +def find_recent_deals_for_address( + address: str, + years_back: int = 2, + radius_meters: int = 30, + max_deals: int = 100, + deal_type: int = 2, +) -> str: """Find recent real estate deals for a specific address. - + Args: address: The address to search for (in Hebrew or English) years_back: How many years back to search (default: 2) @@ -189,12 +211,14 @@ def find_recent_deals_for_address(address: str, years_back: int = 2, radius_mete Small radius since street deals cover the entire street anyway max_deals: Maximum number of deals to return (default: 100, provides good context for LLM analysis) deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2) - + Returns: JSON string containing recent real estate deals for the address """ try: - deals = client.find_recent_deals_for_address(address, years_back, radius_meters, max_deals, deal_type) + deals = client.find_recent_deals_for_address( + address, years_back, radius_meters, max_deals, deal_type + ) if not deals: deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)" @@ -207,74 +231,95 @@ def find_recent_deals_for_address(address: str, years_back: int = 2, radius_mete # Separate building, street and neighborhood deals for analysis # deal_source is added dynamically in find_recent_deals_for_address - building_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "same_building"] + building_deals = [ + deal for deal in deals if getattr(deal, "deal_source", None) == "same_building" + ] street_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "street"] - neighborhood_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "neighborhood"] - + neighborhood_deals = [ + deal for deal in deals if getattr(deal, "deal_source", None) == "neighborhood" + ] + stats = { "deal_breakdown": { "total_deals": len(deals), "same_building_deals": len(building_deals), "street_deals": len(street_deals), "neighborhood_deals": len(neighborhood_deals), - "same_building_percentage": round((len(building_deals) / len(deals)) * 100, 1) if deals else 0, - "street_emphasis_percentage": round((len(street_deals) / len(deals)) * 100, 1) if deals else 0, - "neighborhood_percentage": round((len(neighborhood_deals) / len(deals)) * 100, 1) if deals else 0 + "same_building_percentage": round((len(building_deals) / len(deals)) * 100, 1) + if deals + else 0, + "street_emphasis_percentage": round((len(street_deals) / len(deals)) * 100, 1) + if deals + else 0, + "neighborhood_percentage": round((len(neighborhood_deals) / len(deals)) * 100, 1) + if deals + else 0, } } - + if prices: stats["price_stats"] = { "average_price": round(sum(prices) / len(prices), 0), "min_price": min(prices), "max_price": max(prices), - "median_price": sorted(prices)[len(prices)//2] if prices else 0, - "total_volume": sum(prices) + "median_price": sorted(prices)[len(prices) // 2] if prices else 0, + "total_volume": sum(prices), } - + if areas: stats["area_stats"] = { "average_area": round(sum(areas) / len(areas), 1), "min_area": min(areas), "max_area": max(areas), - "median_area": sorted(areas)[len(areas)//2] if areas else 0 + "median_area": sorted(areas)[len(areas) // 2] if areas else 0, } - + if price_per_sqm_values: stats["price_per_sqm_stats"] = { - "average_price_per_sqm": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0), + "average_price_per_sqm": round( + sum(price_per_sqm_values) / len(price_per_sqm_values), 0 + ), "min_price_per_sqm": round(min(price_per_sqm_values), 0), "max_price_per_sqm": round(max(price_per_sqm_values), 0), - "median_price_per_sqm": round(sorted(price_per_sqm_values)[len(price_per_sqm_values)//2], 0) if price_per_sqm_values else 0 + "median_price_per_sqm": round( + sorted(price_per_sqm_values)[len(price_per_sqm_values) // 2], 0 + ) + if price_per_sqm_values + else 0, } - + deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)" - return json.dumps({ - "search_parameters": { - "address": address, - "years_back": years_back, - "radius_meters": radius_meters, - "max_deals": max_deals, - "deal_type": deal_type, - "deal_type_description": deal_type_desc + return json.dumps( + { + "search_parameters": { + "address": address, + "years_back": years_back, + "radius_meters": radius_meters, + "max_deals": max_deals, + "deal_type": deal_type, + "deal_type_description": deal_type_desc, + }, + "market_statistics": stats, + "deals": strip_bloat_fields(deals), }, - "market_statistics": stats, - "deals": strip_bloat_fields(deals) - }, ensure_ascii=False, indent=2) - + ensure_ascii=False, + indent=2, + ) + except Exception as e: logger.error(f"Error in find_recent_deals_for_address: {e}") return f"Error analyzing address: {str(e)}" + @mcp.tool() def get_neighborhood_deals(polygon_id: str, limit: int = 100, deal_type: int = 2) -> str: """Get real estate deals for a specific neighborhood polygon. - + Args: polygon_id: The polygon ID of the neighborhood limit: Maximum number of deals to return (default: 100) deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2) - + Returns: JSON string containing recent real estate deals in the specified neighborhood """ @@ -288,63 +333,79 @@ def get_neighborhood_deals(polygon_id: str, limit: int = 100, deal_type: int = 2 # Add deal type metadata for deal in deals: deal.deal_type = deal_type - deal.deal_type_description = 'first_hand_new' if deal_type == 1 else 'second_hand_used' + deal.deal_type_description = "first_hand_new" if deal_type == 1 else "second_hand_used" # Calculate basic statistics using computed fields from models - prices = [deal.deal_amount for deal in deals if deal.deal_amount] price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm] - + stats = {} if price_per_sqm_values: stats["price_per_sqm_stats"] = { - "average_price_per_sqm": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0), + "average_price_per_sqm": round( + sum(price_per_sqm_values) / len(price_per_sqm_values), 0 + ), "min_price_per_sqm": round(min(price_per_sqm_values), 0), - "max_price_per_sqm": round(max(price_per_sqm_values), 0) + "max_price_per_sqm": round(max(price_per_sqm_values), 0), } - + deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)" - return json.dumps({ - "total_deals": len(deals), - "polygon_id": polygon_id, - "deal_type": deal_type, - "deal_type_description": deal_type_desc, - "market_statistics": stats, - "deals": strip_bloat_fields(deals) - }, ensure_ascii=False, indent=2) + return json.dumps( + { + "total_deals": len(deals), + "polygon_id": polygon_id, + "deal_type": deal_type, + "deal_type_description": deal_type_desc, + "market_statistics": stats, + "deals": strip_bloat_fields(deals), + }, + ensure_ascii=False, + indent=2, + ) except Exception as e: logger.error(f"Error in get_neighborhood_deals: {e}") return f"Error fetching neighborhood deals: {str(e)}" + @mcp.tool() -def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int = 100, max_deals: int = 100, deal_type: int = 2) -> str: +def analyze_market_trends( + address: str, + years_back: int = 3, + radius_meters: int = 100, + max_deals: int = 100, + deal_type: int = 2, +) -> str: """Analyze market trends and price patterns for an area with comprehensive data. - + Args: address: The address to analyze trends around years_back: How many years of data to analyze (default: 3) radius_meters: Search radius in meters from the address (default: 100, larger for trend analysis) max_deals: Maximum number of deals to analyze (default: 100, optimized for performance and token limits) deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2) - + Returns: JSON string containing comprehensive market trend analysis (summarized data, not raw deals) """ try: # Get deals for the address with larger radius for trend analysis - deals = client.find_recent_deals_for_address(address, years_back, radius_meters, max_deals, deal_type) - + deals = client.find_recent_deals_for_address( + address, years_back, radius_meters, max_deals, deal_type + ) + if not deals: deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)" return f"No {deal_type_desc} deals found for comprehensive market analysis near '{address}'" - + # Efficient analysis with reduced complexity from collections import defaultdict - + yearly_data = defaultdict(list) - property_types: Dict[str, List[float]] = defaultdict(list) # Store only prices for efficiency + property_types: Dict[str, List[float]] = defaultdict( + list + ) # Store only prices for efficiency neighborhoods = defaultdict(list) - + # Simplified processing - extract only essential data for deal in deals: if not deal.deal_date: @@ -352,167 +413,242 @@ def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int # Convert date to string for parsing from datetime import date as date_type - date_str = deal.deal_date.isoformat() if isinstance(deal.deal_date, date_type) else str(deal.deal_date) + + date_str = ( + deal.deal_date.isoformat() + if isinstance(deal.deal_date, date_type) + else str(deal.deal_date) + ) year = date_str[:4] price = deal.deal_amount area = deal.asset_area price_per_sqm = deal.price_per_sqm - prop_type = deal.property_type_description or 'לא ידוע' - neighborhood = deal.settlement_name_heb or deal.neighborhood or 'לא ידוע' - deal_source = getattr(deal, 'deal_source', 'unknown') - - if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0 and isinstance(price_per_sqm, (int, float)): - yearly_data[year].append({ - 'price': price, 'area': area, 'price_per_sqm': price_per_sqm, 'deal_source': deal_source - }) + prop_type = deal.property_type_description or "לא ידוע" + neighborhood = deal.settlement_name_heb or deal.neighborhood or "לא ידוע" + deal_source = getattr(deal, "deal_source", "unknown") + + if ( + isinstance(price, (int, float)) + and isinstance(area, (int, float)) + and area > 0 + and isinstance(price_per_sqm, (int, float)) + ): + yearly_data[year].append( + { + "price": price, + "area": area, + "price_per_sqm": price_per_sqm, + "deal_source": deal_source, + } + ) property_types[prop_type].append(price_per_sqm) neighborhoods[neighborhood].append(price_per_sqm) - + # Calculate streamlined yearly trends yearly_trends = {} for year, year_deals in yearly_data.items(): if year_deals: - prices = [d['price'] for d in year_deals] - price_per_sqm_vals = [d['price_per_sqm'] for d in year_deals] - building_deals = [d for d in year_deals if d['deal_source'] == 'same_building'] - street_deals = [d for d in year_deals if d['deal_source'] == 'street'] - + prices = [d["price"] for d in year_deals] + price_per_sqm_vals = [d["price_per_sqm"] for d in year_deals] + building_deals = [d for d in year_deals if d["deal_source"] == "same_building"] + street_deals = [d for d in year_deals if d["deal_source"] == "street"] + yearly_trends[year] = { "deal_count": len(year_deals), "same_building_deals": len(building_deals), "street_deals": len(street_deals), "avg_price": round(sum(prices) / len(prices), 0), - "avg_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0), + "avg_price_per_sqm": round( + sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0 + ), "min_price_per_sqm": round(min(price_per_sqm_vals), 0), "max_price_per_sqm": round(max(price_per_sqm_vals), 0), - "total_volume": sum(prices) + "total_volume": sum(prices), } - + # Streamlined property type analysis (top 5 only) property_type_analysis = {} for prop_type, prices_per_sqm in property_types.items(): if len(prices_per_sqm) >= 2: # Only include types with multiple deals property_type_analysis[prop_type] = { "deal_count": len(prices_per_sqm), - "avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0) + "avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0), } - + # Keep only top 5 property types by deal count - property_type_analysis = dict(sorted(property_type_analysis.items(), - key=lambda x: x[1]['deal_count'], reverse=True)[:5]) - + property_type_analysis = dict( + sorted(property_type_analysis.items(), key=lambda x: x[1]["deal_count"], reverse=True)[ + :5 + ] + ) + # Streamlined neighborhood analysis (top 5 only) neighborhood_analysis = {} for neighborhood, prices_per_sqm in neighborhoods.items(): if len(prices_per_sqm) >= 3: # Minimum 3 deals for statistical significance neighborhood_analysis[neighborhood] = { "deal_count": len(prices_per_sqm), - "avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0) + "avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0), } - + # Keep only top 5 neighborhoods by deal count - neighborhood_analysis = dict(sorted(neighborhood_analysis.items(), - key=lambda x: x[1]['deal_count'], reverse=True)[:5]) - + neighborhood_analysis = dict( + sorted(neighborhood_analysis.items(), key=lambda x: x[1]["deal_count"], reverse=True)[ + :5 + ] + ) + # Simple trend analysis years_sorted = sorted(yearly_trends.keys()) trend_analysis = {} if len(years_sorted) >= 2: first_year = yearly_trends[years_sorted[0]] last_year = yearly_trends[years_sorted[-1]] - - if first_year['avg_price_per_sqm'] > 0: - price_change = ((last_year['avg_price_per_sqm'] - first_year['avg_price_per_sqm']) / first_year['avg_price_per_sqm']) * 100 - volume_change = ((last_year['deal_count'] - first_year['deal_count']) / first_year['deal_count']) * 100 if first_year['deal_count'] > 0 else 0 - + + if first_year["avg_price_per_sqm"] > 0: + price_change = ( + (last_year["avg_price_per_sqm"] - first_year["avg_price_per_sqm"]) + / first_year["avg_price_per_sqm"] + ) * 100 + volume_change = ( + ( + (last_year["deal_count"] - first_year["deal_count"]) + / first_year["deal_count"] + ) + * 100 + if first_year["deal_count"] > 0 + else 0 + ) + trend_analysis = { "price_trend_percentage": round(price_change, 1), "volume_trend_percentage": round(volume_change, 1), - "first_year_avg_price_per_sqm": first_year['avg_price_per_sqm'], - "last_year_avg_price_per_sqm": last_year['avg_price_per_sqm'] + "first_year_avg_price_per_sqm": first_year["avg_price_per_sqm"], + "last_year_avg_price_per_sqm": last_year["avg_price_per_sqm"], } - + deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)" - + # Return summarized analysis (NO raw deals to save tokens) - return json.dumps({ - "analysis_parameters": { - "address": address, - "years_analyzed": years_back, - "radius_meters": radius_meters, - "deals_analyzed": len(deals), - "deal_type": deal_type, - "deal_type_description": deal_type_desc + return json.dumps( + { + "analysis_parameters": { + "address": address, + "years_analyzed": years_back, + "radius_meters": radius_meters, + "deals_analyzed": len(deals), + "deal_type": deal_type, + "deal_type_description": deal_type_desc, + }, + "market_summary": { + "total_deals": len(deals), + "years_with_data": len(yearly_trends), + "unique_property_types": len(property_type_analysis), + "unique_neighborhoods": len(neighborhood_analysis), + }, + "yearly_trends": yearly_trends, + "top_property_types": property_type_analysis, + "top_neighborhoods": neighborhood_analysis, + "trend_analysis": trend_analysis, + "key_insights": { + "most_active_year": max( + yearly_trends.keys(), key=lambda y: yearly_trends[y]["deal_count"] + ) + if yearly_trends + else None, + "highest_avg_price_year": max( + yearly_trends.keys(), key=lambda y: yearly_trends[y]["avg_price_per_sqm"] + ) + if yearly_trends + else None, + "deal_source_summary": f"Building: {len([d for d in deals if getattr(d, 'deal_source', None) == 'same_building'])}, Street: {len([d for d in deals if getattr(d, 'deal_source', None) == 'street'])}, Neighborhood: {len([d for d in deals if getattr(d, 'deal_source', None) == 'neighborhood'])}", + }, }, - "market_summary": { - "total_deals": len(deals), - "years_with_data": len(yearly_trends), - "unique_property_types": len(property_type_analysis), - "unique_neighborhoods": len(neighborhood_analysis) - }, - "yearly_trends": yearly_trends, - "top_property_types": property_type_analysis, - "top_neighborhoods": neighborhood_analysis, - "trend_analysis": trend_analysis, - "key_insights": { - "most_active_year": max(yearly_trends.keys(), key=lambda y: yearly_trends[y]['deal_count']) if yearly_trends else None, - "highest_avg_price_year": max(yearly_trends.keys(), key=lambda y: yearly_trends[y]['avg_price_per_sqm']) if yearly_trends else None, - "deal_source_summary": f"Building: {len([d for d in deals if getattr(d, 'deal_source', None) == 'same_building'])}, Street: {len([d for d in deals if getattr(d, 'deal_source', None) == 'street'])}, Neighborhood: {len([d for d in deals if getattr(d, 'deal_source', None) == 'neighborhood'])}" - } - }, ensure_ascii=False, indent=2) - + ensure_ascii=False, + indent=2, + ) + except Exception as e: logger.error(f"Error in analyze_market_trends: {e}") return f"Error analyzing market trends: {str(e)}" + @mcp.tool() def compare_addresses(addresses: List[str]) -> str: """Compare real estate markets between multiple addresses. - + Args: addresses: List of addresses to compare (in Hebrew or English) - + Returns: JSON string containing comparative analysis of multiple addresses """ try: comparisons = [] - + for address in addresses: try: deals = client.find_recent_deals_for_address(address, 2) - + if deals: prices = [deal.deal_amount for deal in deals if deal.deal_amount] areas = [deal.asset_area for deal in deals if deal.asset_area] - price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm] - building_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "same_building"] - street_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "street"] - neighborhood_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "neighborhood"] - + price_per_sqm_values = [ + deal.price_per_sqm for deal in deals if deal.price_per_sqm + ] + building_deals = [ + deal + for deal in deals + if getattr(deal, "deal_source", None) == "same_building" + ] + street_deals = [ + deal for deal in deals if getattr(deal, "deal_source", None) == "street" + ] + neighborhood_deals = [ + deal + for deal in deals + if getattr(deal, "deal_source", None) == "neighborhood" + ] + comparison = { "address": address, "total_deals": len(deals), "same_building_deals": len(building_deals), "street_deals": len(street_deals), "neighborhood_deals": len(neighborhood_deals), - "same_building_percentage": round((len(building_deals) / len(deals)) * 100, 1) if deals else 0, - "street_emphasis_percentage": round((len(street_deals) / len(deals)) * 100, 1) if deals else 0, + "same_building_percentage": round( + (len(building_deals) / len(deals)) * 100, 1 + ) + if deals + else 0, + "street_emphasis_percentage": round( + (len(street_deals) / len(deals)) * 100, 1 + ) + if deals + else 0, "price_stats": { "average_price": round(sum(prices) / len(prices), 0) if prices else 0, "min_price": min(prices) if prices else 0, - "max_price": max(prices) if prices else 0 + "max_price": max(prices) if prices else 0, }, "area_stats": { "average_area": round(sum(areas) / len(areas), 1) if areas else 0, "min_area": min(areas) if areas else 0, - "max_area": max(areas) if areas else 0 + "max_area": max(areas) if areas else 0, }, "price_per_sqm_stats": { - "average_price_per_sqm": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0) if price_per_sqm_values else 0, - "min_price_per_sqm": round(min(price_per_sqm_values), 0) if price_per_sqm_values else 0, - "max_price_per_sqm": round(max(price_per_sqm_values), 0) if price_per_sqm_values else 0 - } + "average_price_per_sqm": round( + sum(price_per_sqm_values) / len(price_per_sqm_values), 0 + ) + if price_per_sqm_values + else 0, + "min_price_per_sqm": round(min(price_per_sqm_values), 0) + if price_per_sqm_values + else 0, + "max_price_per_sqm": round(max(price_per_sqm_values), 0) + if price_per_sqm_values + else 0, + }, } else: comparison = { @@ -525,46 +661,50 @@ def compare_addresses(addresses: List[str]) -> str: "street_emphasis_percentage": 0, "price_stats": {}, "area_stats": {}, - "price_per_sqm_stats": {} + "price_per_sqm_stats": {}, } - + comparisons.append(comparison) - + except Exception as e: logger.error(f"Error comparing {address}: {e}") - comparisons.append({ - "address": address, - "error": str(e) - }) - + comparisons.append({"address": address, "error": str(e)}) + # Rank addresses by average price per sqm valid_comparisons = [] for comparison in comparisons: - if (isinstance(comparison, dict) and - "price_per_sqm_stats" in comparison and - isinstance(comparison["price_per_sqm_stats"], dict) and - comparison["price_per_sqm_stats"].get("average_price_per_sqm", 0) > 0): + if ( + isinstance(comparison, dict) + and "price_per_sqm_stats" in comparison + and isinstance(comparison["price_per_sqm_stats"], dict) + and comparison["price_per_sqm_stats"].get("average_price_per_sqm", 0) > 0 + ): valid_comparisons.append(comparison) - + # Sort by price per sqm def get_price_per_sqm(comp: dict) -> float: price_stats = comp.get("price_per_sqm_stats", {}) if isinstance(price_stats, dict): return price_stats.get("average_price_per_sqm", 0) return 0 - + valid_comparisons.sort(key=get_price_per_sqm, reverse=True) - - return json.dumps({ - "addresses_compared": len(addresses), - "ranking_by_average_price_per_sqm": valid_comparisons, - "all_results": comparisons - }, ensure_ascii=False, indent=2) - + + return json.dumps( + { + "addresses_compared": len(addresses), + "ranking_by_average_price_per_sqm": valid_comparisons, + "all_results": comparisons, + }, + ensure_ascii=False, + indent=2, + ) + except Exception as e: logger.error(f"Error in compare_addresses: {e}") return f"Error comparing addresses: {str(e)}" + @mcp.tool() def get_valuation_comparables( address: str, @@ -579,7 +719,7 @@ def get_valuation_comparables( min_floor: Optional[int] = None, max_floor: Optional[int] = None, radius_meters: int = 100, - max_comparables: int = 50 + max_comparables: int = 50, ) -> str: """Get comparable properties for valuation analysis. @@ -609,20 +749,21 @@ def get_valuation_comparables( try: # Get all deals for the address with higher limits for valuation deals = client.find_recent_deals_for_address( - address, - years_back, - radius=radius_meters, - max_deals=max_comparables + address, years_back, radius=radius_meters, max_deals=max_comparables ) - + if not deals: - return json.dumps({ - "address": address, - "years_back": years_back, - "comparables": [], - "message": "No deals found for this address" - }, ensure_ascii=False, indent=2) - + return json.dumps( + { + "address": address, + "years_back": years_back, + "comparables": [], + "message": "No deals found for this address", + }, + ensure_ascii=False, + indent=2, + ) + # Apply filters filtered_deals = client.filter_deals_by_criteria( deals, @@ -634,90 +775,97 @@ def get_valuation_comparables( min_area=min_area, max_area=max_area, min_floor=min_floor, - max_floor=max_floor + max_floor=max_floor, ) - + # Calculate statistics on filtered comparables stats = client.calculate_deal_statistics(filtered_deals) - return json.dumps({ - "address": address, - "years_back": years_back, - "filters_applied": { - "property_type": property_type, - "rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None, - "price": f"{min_price}-{max_price}" if min_price or max_price else None, - "area": f"{min_area}-{max_area}" if min_area or max_area else None, - "floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None, + return json.dumps( + { + "address": address, + "years_back": years_back, + "filters_applied": { + "property_type": property_type, + "rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None, + "price": f"{min_price}-{max_price}" if min_price or max_price else None, + "area": f"{min_area}-{max_area}" if min_area or max_area else None, + "floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None, + }, + "total_comparables": len(filtered_deals), + "statistics": stats.model_dump(exclude_none=True), # Serialize DealStatistics model + "comparables": strip_bloat_fields(filtered_deals), }, - "total_comparables": len(filtered_deals), - "statistics": stats.model_dump(exclude_none=True), # Serialize DealStatistics model - "comparables": strip_bloat_fields(filtered_deals) - }, ensure_ascii=False, indent=2) + ensure_ascii=False, + indent=2, + ) except Exception as e: logger.error(f"Error in get_valuation_comparables: {e}") return f"Error getting valuation comparables: {str(e)}" + @mcp.tool() def get_deal_statistics( address: str, years_back: int = 2, property_type: Optional[str] = None, min_rooms: Optional[float] = None, - max_rooms: Optional[float] = None + max_rooms: Optional[float] = None, ) -> str: """Calculate statistical aggregations on deal data for an address. - + This tool provides quick statistical summaries without returning all raw deals. Useful when LLM needs calculations on large datasets without full details. - + Args: address: The address to analyze (in Hebrew or English) years_back: How many years back to analyze (default: 2) property_type: Filter by property type (e.g., "דירה", "בית") min_rooms: Minimum number of rooms max_rooms: Maximum number of rooms - + Returns: JSON string containing statistical metrics (mean, median, percentiles, etc.) """ try: # Get all deals for the address deals = client.find_recent_deals_for_address(address, years_back) - + if not deals: - return json.dumps({ - "address": address, - "years_back": years_back, - "statistics": { - "count": 0, - "message": "No deals found for this address" - } - }, ensure_ascii=False, indent=2) - + return json.dumps( + { + "address": address, + "years_back": years_back, + "statistics": {"count": 0, "message": "No deals found for this address"}, + }, + ensure_ascii=False, + indent=2, + ) + # Apply filters if provided if property_type or min_rooms or max_rooms: deals = client.filter_deals_by_criteria( - deals, - property_type=property_type, - min_rooms=min_rooms, - max_rooms=max_rooms + deals, property_type=property_type, min_rooms=min_rooms, max_rooms=max_rooms ) - + # Calculate statistics stats = client.calculate_deal_statistics(deals) - return json.dumps({ - "address": address, - "years_back": years_back, - "filters_applied": { - "property_type": property_type, - "rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None, + return json.dumps( + { + "address": address, + "years_back": years_back, + "filters_applied": { + "property_type": property_type, + "rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None, + }, + "statistics": stats.model_dump(exclude_none=True), # Serialize DealStatistics model }, - "statistics": stats.model_dump(exclude_none=True) # Serialize DealStatistics model - }, ensure_ascii=False, indent=2) - + ensure_ascii=False, + indent=2, + ) + except Exception as e: logger.error(f"Error in get_deal_statistics: {e}") return f"Error calculating deal statistics: {str(e)}" @@ -741,7 +889,7 @@ def _safe_calculate_metric(metric_func, deals): try: result = metric_func(deals) # Serialize Pydantic model to dict - if hasattr(result, 'model_dump'): + if hasattr(result, "model_dump"): return result.model_dump(exclude_none=True) return result except ValueError as e: @@ -749,11 +897,7 @@ def _safe_calculate_metric(metric_func, deals): @mcp.tool() -def get_market_activity_metrics( - address: str, - years_back: int = 2, - radius_meters: int = 100 -) -> str: +def get_market_activity_metrics(address: str, years_back: int = 2, radius_meters: int = 100) -> str: """Get comprehensive market activity and investment potential analysis. This tool provides detailed market liquidity, activity scores, and investment @@ -777,12 +921,16 @@ def get_market_activity_metrics( deals = client.find_recent_deals_for_address(address, years_back, radius_meters) if not deals: - return json.dumps({ - "address": address, - "error": "No deals found for analysis", - "years_back": years_back, - "radius_meters": radius_meters - }, ensure_ascii=False, indent=2) + return json.dumps( + { + "address": address, + "error": "No deals found for analysis", + "years_back": years_back, + "radius_meters": radius_meters, + }, + ensure_ascii=False, + indent=2, + ) # Calculate market metrics using helper to reduce duplication activity_metrics = _safe_calculate_metric(client.calculate_market_activity_score, deals) @@ -790,29 +938,34 @@ def get_market_activity_metrics( investment_metrics = _safe_calculate_metric(client.analyze_investment_potential, deals) # Combine all metrics - return json.dumps({ - "address": address, - "years_back": years_back, - "radius_meters": radius_meters, - "total_deals_analyzed": len(deals), - "market_activity": activity_metrics, - "market_liquidity": liquidity_metrics, - "investment_potential": investment_metrics, - "summary": { - "activity_score": activity_metrics.get("activity_score"), - "activity_trend": activity_metrics.get("trend"), - "liquidity_score": liquidity_metrics.get("liquidity_score"), - "market_activity_level": liquidity_metrics.get("market_activity_level"), - "investment_score": investment_metrics.get("investment_score"), - "price_trend": investment_metrics.get("price_trend"), - "market_stability": investment_metrics.get("market_stability") - } - }, ensure_ascii=False, indent=2) + return json.dumps( + { + "address": address, + "years_back": years_back, + "radius_meters": radius_meters, + "total_deals_analyzed": len(deals), + "market_activity": activity_metrics, + "market_liquidity": liquidity_metrics, + "investment_potential": investment_metrics, + "summary": { + "activity_score": activity_metrics.get("activity_score"), + "activity_trend": activity_metrics.get("trend"), + "liquidity_score": liquidity_metrics.get("liquidity_score"), + "market_activity_level": liquidity_metrics.get("market_activity_level"), + "investment_score": investment_metrics.get("investment_score"), + "price_trend": investment_metrics.get("price_trend"), + "market_stability": investment_metrics.get("market_stability"), + }, + }, + ensure_ascii=False, + indent=2, + ) except Exception as e: logger.error(f"Error in get_market_activity_metrics: {e}") return f"Error analyzing market activity: {str(e)}" + # Run the server if __name__ == "__main__": mcp.run() diff --git a/nadlan_mcp/govmap/__init__.py b/nadlan_mcp/govmap/__init__.py index 38eb652..f9e942e 100644 --- a/nadlan_mcp/govmap/__init__.py +++ b/nadlan_mcp/govmap/__init__.py @@ -15,47 +15,46 @@ Public API: """ # Pydantic models -from .models import ( - CoordinatePoint, - Address, - AutocompleteResult, - AutocompleteResponse, - Deal, - DealStatistics, - MarketActivityScore, - InvestmentAnalysis, - LiquidityMetrics, - DealFilters, -) +# Main API client +from .client import GovmapClient # Filter functions from .filters import filter_deals_by_criteria -# Statistics functions -from .statistics import calculate_deal_statistics, calculate_std_dev - # Market analysis functions from .market_analysis import ( - calculate_market_activity_score, analyze_investment_potential, + calculate_market_activity_score, get_market_liquidity, parse_deal_dates, ) +from .models import ( + Address, + AutocompleteResponse, + AutocompleteResult, + CoordinatePoint, + Deal, + DealFilters, + DealStatistics, + InvestmentAnalysis, + LiquidityMetrics, + MarketActivityScore, +) + +# Statistics functions +from .statistics import calculate_deal_statistics, calculate_std_dev # Utility functions -from .utils import calculate_distance, is_same_building, extract_floor_number +from .utils import calculate_distance, extract_floor_number, is_same_building # Validation functions from .validators import ( validate_address, validate_coordinates, - validate_positive_int, validate_deal_type, + validate_positive_int, ) -# Main API client -from .client import GovmapClient - __all__ = [ # Main client class "GovmapClient", diff --git a/nadlan_mcp/govmap/client.py b/nadlan_mcp/govmap/client.py index b4f284f..284f11b 100644 --- a/nadlan_mcp/govmap/client.py +++ b/nadlan_mcp/govmap/client.py @@ -6,34 +6,30 @@ Israeli government's Govmap API to retrieve property deals, market trends, and real estate information. """ +from datetime import datetime, timedelta import logging import time from typing import Any, Dict, List, Optional, Tuple -from datetime import datetime, timedelta import requests from nadlan_mcp.config import GovmapConfig, get_config +# Import functions from modular package +from . import filters, market_analysis, statistics, utils, validators + # Import models from .models import ( - Deal, AutocompleteResponse, AutocompleteResult, CoordinatePoint, + Deal, DealStatistics, - MarketActivityScore, InvestmentAnalysis, LiquidityMetrics, + MarketActivityScore, ) -# Import functions from modular package -from . import validators -from . import utils -from . import filters -from . import statistics -from . import market_analysis - logger = logging.getLogger(__name__) @@ -86,9 +82,7 @@ class GovmapClient: """Validate coordinate input.""" return validators.validate_coordinates(point) - def _validate_positive_int( - self, value: int, name: str, max_value: Optional[int] = None - ) -> int: + def _validate_positive_int(self, value: int, name: str, max_value: Optional[int] = None) -> int: """Validate positive integer input.""" return validators.validate_positive_int(value, name, max_value) @@ -160,24 +154,26 @@ class GovmapClient: coords = coords_str.split() if len(coords) == 2: coordinates = CoordinatePoint( - longitude=float(coords[0]), - latitude=float(coords[1]) + longitude=float(coords[0]), latitude=float(coords[1]) ) except (ValueError, IndexError) as e: - logger.warning(f"Failed to parse coordinates from shape: {shape_str}, error: {e}") + logger.warning( + f"Failed to parse coordinates from shape: {shape_str}, error: {e}" + ) - results.append(AutocompleteResult( - text=result.get("text", ""), - id=result.get("id", ""), - type=result.get("type", ""), - score=result.get("score", 0), - coordinates=coordinates, - shape=shape_str if shape_str else None, - )) + results.append( + AutocompleteResult( + text=result.get("text", ""), + id=result.get("id", ""), + type=result.get("type", ""), + score=result.get("score", 0), + coordinates=coordinates, + shape=shape_str if shape_str else None, + ) + ) return AutocompleteResponse( - resultsCount=data.get("resultsCount", len(results)), - results=results + resultsCount=data.get("resultsCount", len(results)), results=results ) except (requests.RequestException, requests.Timeout) as e: @@ -196,9 +192,7 @@ class GovmapClient: ) raise # This line should never be reached but satisfies type checker - raise RuntimeError( - "Unexpected error: retry loop exited without return or raise" - ) + raise RuntimeError("Unexpected error: retry loop exited without return or raise") def get_gush_helka(self, point: Tuple[float, float]) -> Dict[str, Any]: """ @@ -250,9 +244,7 @@ class GovmapClient: ) raise # This line should never be reached but satisfies type checker - raise RuntimeError( - "Unexpected error: retry loop exited without return or raise" - ) + raise RuntimeError("Unexpected error: retry loop exited without return or raise") def get_deals_by_radius( self, point: Tuple[float, float], radius: int = 50 @@ -295,9 +287,7 @@ class GovmapClient: data = response.json() if not isinstance(data, list): - raise ValueError( - f"Expected list response, got {type(data).__name__}" - ) + raise ValueError(f"Expected list response, got {type(data).__name__}") # NOTE: This endpoint returns polygon metadata, not actual deals! # The response contains: dealscount, polygon_id, settlementNameHeb, streetNameHeb, houseNum, objectid @@ -326,9 +316,7 @@ class GovmapClient: ) raise # This line should never be reached but satisfies type checker - raise RuntimeError( - "Unexpected error: retry loop exited without return or raise" - ) + raise RuntimeError("Unexpected error: retry loop exited without return or raise") def get_street_deals( self, @@ -396,9 +384,7 @@ class GovmapClient: elif isinstance(data, list): deal_dicts = data else: - raise ValueError( - f"Unexpected response format: {type(data).__name__}" - ) + raise ValueError(f"Unexpected response format: {type(data).__name__}") # Parse each deal dict into Deal model deals = [] @@ -428,9 +414,7 @@ class GovmapClient: ) raise # This line should never be reached but satisfies type checker - raise RuntimeError( - "Unexpected error: retry loop exited without return or raise" - ) + raise RuntimeError("Unexpected error: retry loop exited without return or raise") def get_neighborhood_deals( self, @@ -498,9 +482,7 @@ class GovmapClient: elif isinstance(data, list): deal_dicts = data else: - raise ValueError( - f"Unexpected response format: {type(data).__name__}" - ) + raise ValueError(f"Unexpected response format: {type(data).__name__}") # Parse each deal dict into Deal model deals = [] @@ -530,9 +512,7 @@ class GovmapClient: ) raise # This line should never be reached but satisfies type checker - raise RuntimeError( - "Unexpected error: retry loop exited without return or raise" - ) + raise RuntimeError("Unexpected error: retry loop exited without return or raise") def find_recent_deals_for_address( self, @@ -573,9 +553,7 @@ class GovmapClient: try: # Step 1: Get coordinates for the address - logger.info( - f"Starting search for address: {address}, dealType: {deal_type}" - ) + logger.info(f"Starting search for address: {address}, dealType: {deal_type}") autocomplete_result = self.autocomplete_address(address) if not autocomplete_result.results: @@ -598,7 +576,7 @@ class GovmapClient: polygon_ids = set() for metadata in nearby_polygons: # Extract polygon_id from dict (these are polygon metadata, not deals) - polygon_id = metadata.get('polygon_id') + polygon_id = metadata.get("polygon_id") if polygon_id: polygon_ids.add(str(polygon_id)) @@ -667,9 +645,7 @@ class GovmapClient: street = deal.street_name or "" house_num = str(deal.house_number or "") deal_address = f"{street} {house_num}".lower().strip() - if self._is_same_building( - search_address_normalized, deal_address - ): + if self._is_same_building(search_address_normalized, deal_address): deal.deal_source = "same_building" deal.priority = 0 # Highest priority building_deals.append(deal) @@ -698,11 +674,9 @@ class GovmapClient: # Use stable sort: first by date (newest first), then by priority # Since Python's sort is stable, the second sort maintains date order within each priority + all_deals.sort(key=lambda x: x.deal_date or "1900-01-01", reverse=True) # Newest first all_deals.sort( - key=lambda x: x.deal_date or "1900-01-01", reverse=True - ) # Newest first - all_deals.sort( - key=lambda x: getattr(x, 'priority', 3) + key=lambda x: getattr(x, "priority", 3) ) # Priority first (0=building, 1=street, 2=neighborhood) # Limit to max_deals @@ -799,9 +773,7 @@ class GovmapClient: return statistics.calculate_std_dev(values) # Market analysis methods (delegate to market_analysis module) - def _parse_deal_dates( - self, deals: List[Deal], time_period_months: Optional[int] = None - ): + def _parse_deal_dates(self, deals: List[Deal], time_period_months: Optional[int] = None): """ Parse and filter deal dates from a list of deals. @@ -831,13 +803,9 @@ class GovmapClient: Returns: MarketActivityScore model with activity metrics """ - return market_analysis.calculate_market_activity_score( - deals, time_period_months - ) + return market_analysis.calculate_market_activity_score(deals, time_period_months) - def analyze_investment_potential( - self, deals: List[Deal] - ) -> InvestmentAnalysis: + def analyze_investment_potential(self, deals: List[Deal]) -> InvestmentAnalysis: """ Analyze investment potential based on price trends and market stability. diff --git a/nadlan_mcp/govmap/filters.py b/nadlan_mcp/govmap/filters.py index 247b9ce..e152a7e 100644 --- a/nadlan_mcp/govmap/filters.py +++ b/nadlan_mcp/govmap/filters.py @@ -94,9 +94,12 @@ def filter_deals_by_criteria( # Handle Hebrew feminine ending variations (ה ↔ ת) # If the filter term ends with ה, also check for the ת variant # This allows "דירה" to match "דירת גג", "דירה בבניין", etc. - if property_type_normalized.endswith('ה'): - property_type_variant = property_type_normalized[:-1] + 'ת' - if property_type_variant not in deal_type_normalized and property_type_normalized not in deal_type_normalized: + if property_type_normalized.endswith("ה"): + property_type_variant = property_type_normalized[:-1] + "ת" + if ( + property_type_variant not in deal_type_normalized + and property_type_normalized not in deal_type_normalized + ): # No match found for either variant continue else: diff --git a/nadlan_mcp/govmap/market_analysis.py b/nadlan_mcp/govmap/market_analysis.py index 9fef4a4..ecd0ef5 100644 --- a/nadlan_mcp/govmap/market_analysis.py +++ b/nadlan_mcp/govmap/market_analysis.py @@ -5,12 +5,12 @@ This module provides functions for analyzing market trends, activity, and invest Focused on providing data metrics; the LLM interprets them for investment advice. """ -import logging from collections import defaultdict from datetime import date, datetime, timedelta +import logging from typing import Dict, List, Optional, Tuple -from .models import Deal, MarketActivityScore, InvestmentAnalysis, LiquidityMetrics +from .models import Deal, InvestmentAnalysis, LiquidityMetrics, MarketActivityScore from .statistics import calculate_std_dev logger = logging.getLogger(__name__) @@ -73,7 +73,11 @@ def parse_deal_dates( try: # Convert date to string for comparison and parsing - date_str = deal.deal_date.isoformat() if isinstance(deal.deal_date, date) else str(deal.deal_date) + date_str = ( + deal.deal_date.isoformat() + if isinstance(deal.deal_date, date) + else str(deal.deal_date) + ) # Filter by time period if specified if cutoff_date is not None and date_str < cutoff_date_str: @@ -141,11 +145,27 @@ def calculate_market_activity_score( if deals_per_month >= ACTIVITY_VERY_HIGH_THRESHOLD: activity_score = 100 elif deals_per_month >= ACTIVITY_HIGH_THRESHOLD: - activity_score = 75 + ((deals_per_month - ACTIVITY_HIGH_THRESHOLD) / ACTIVITY_HIGH_THRESHOLD) * 25 + activity_score = ( + 75 + ((deals_per_month - ACTIVITY_HIGH_THRESHOLD) / ACTIVITY_HIGH_THRESHOLD) * 25 + ) elif deals_per_month >= ACTIVITY_MODERATE_THRESHOLD: - activity_score = 50 + ((deals_per_month - ACTIVITY_MODERATE_THRESHOLD) / (ACTIVITY_HIGH_THRESHOLD - ACTIVITY_MODERATE_THRESHOLD)) * 25 + activity_score = ( + 50 + + ( + (deals_per_month - ACTIVITY_MODERATE_THRESHOLD) + / (ACTIVITY_HIGH_THRESHOLD - ACTIVITY_MODERATE_THRESHOLD) + ) + * 25 + ) elif deals_per_month >= ACTIVITY_LOW_THRESHOLD: - activity_score = 25 + ((deals_per_month - ACTIVITY_LOW_THRESHOLD) / (ACTIVITY_MODERATE_THRESHOLD - ACTIVITY_LOW_THRESHOLD)) * 25 + activity_score = ( + 25 + + ( + (deals_per_month - ACTIVITY_LOW_THRESHOLD) + / (ACTIVITY_MODERATE_THRESHOLD - ACTIVITY_LOW_THRESHOLD) + ) + * 25 + ) else: activity_score = deals_per_month * 25 @@ -158,7 +178,9 @@ def calculate_market_activity_score( len(sorted_months) - mid_point ) - change_ratio = (second_half_avg - first_half_avg) / first_half_avg if first_half_avg > 0 else 0 + change_ratio = ( + (second_half_avg - first_half_avg) / first_half_avg if first_half_avg > 0 else 0 + ) if change_ratio > 0.15: trend = "increasing" @@ -215,7 +237,11 @@ def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis: if price_per_sqm and price_per_sqm > 0 and deal.deal_date: try: # Convert date to string for parsing - date_str = deal.deal_date.isoformat() if isinstance(deal.deal_date, date) else str(deal.deal_date) + date_str = ( + deal.deal_date.isoformat() + if isinstance(deal.deal_date, date) + else str(deal.deal_date) + ) # Parse date for sorting year = int(date_str[:4]) @@ -279,13 +305,34 @@ def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis: volatility_score = 100 market_stability = "very_volatile" elif coefficient_of_variation > VOLATILITY_VOLATILE_THRESHOLD: - volatility_score = 75 + ((coefficient_of_variation - VOLATILITY_VOLATILE_THRESHOLD) / (VOLATILITY_VERY_VOLATILE_THRESHOLD - VOLATILITY_VOLATILE_THRESHOLD)) * 25 + volatility_score = ( + 75 + + ( + (coefficient_of_variation - VOLATILITY_VOLATILE_THRESHOLD) + / (VOLATILITY_VERY_VOLATILE_THRESHOLD - VOLATILITY_VOLATILE_THRESHOLD) + ) + * 25 + ) market_stability = "volatile" elif coefficient_of_variation > VOLATILITY_MODERATE_THRESHOLD: - volatility_score = 50 + ((coefficient_of_variation - VOLATILITY_MODERATE_THRESHOLD) / (VOLATILITY_VOLATILE_THRESHOLD - VOLATILITY_MODERATE_THRESHOLD)) * 25 + volatility_score = ( + 50 + + ( + (coefficient_of_variation - VOLATILITY_MODERATE_THRESHOLD) + / (VOLATILITY_VOLATILE_THRESHOLD - VOLATILITY_MODERATE_THRESHOLD) + ) + * 25 + ) market_stability = "moderate" elif coefficient_of_variation > VOLATILITY_STABLE_THRESHOLD: - volatility_score = 25 + ((coefficient_of_variation - VOLATILITY_STABLE_THRESHOLD) / (VOLATILITY_MODERATE_THRESHOLD - VOLATILITY_STABLE_THRESHOLD)) * 25 + volatility_score = ( + 25 + + ( + (coefficient_of_variation - VOLATILITY_STABLE_THRESHOLD) + / (VOLATILITY_MODERATE_THRESHOLD - VOLATILITY_STABLE_THRESHOLD) + ) + * 25 + ) market_stability = "stable" else: volatility_score = (coefficient_of_variation / VOLATILITY_STABLE_THRESHOLD) * 25 @@ -358,10 +405,8 @@ def get_market_liquidity( # Calculate metrics total_deals = len(deal_dates) unique_months = len(monthly_deals) - unique_quarters = len(quarterly_deals) deals_per_month = total_deals / unique_months if unique_months > 0 else 0 - deals_per_quarter = total_deals / unique_quarters if unique_quarters > 0 else 0 # Calculate velocity score (similar to activity score but focused on turnover) # Based on monthly deal velocity using defined thresholds @@ -369,13 +414,34 @@ def get_market_liquidity( velocity_score = 100 liquidity_rating = "very_high" elif deals_per_month >= LIQUIDITY_HIGH_THRESHOLD: - velocity_score = 75 + ((deals_per_month - LIQUIDITY_HIGH_THRESHOLD) / (LIQUIDITY_VERY_HIGH_THRESHOLD - LIQUIDITY_HIGH_THRESHOLD)) * 25 + velocity_score = ( + 75 + + ( + (deals_per_month - LIQUIDITY_HIGH_THRESHOLD) + / (LIQUIDITY_VERY_HIGH_THRESHOLD - LIQUIDITY_HIGH_THRESHOLD) + ) + * 25 + ) liquidity_rating = "high" elif deals_per_month >= LIQUIDITY_MODERATE_THRESHOLD: - velocity_score = 50 + ((deals_per_month - LIQUIDITY_MODERATE_THRESHOLD) / (LIQUIDITY_HIGH_THRESHOLD - LIQUIDITY_MODERATE_THRESHOLD)) * 25 + velocity_score = ( + 50 + + ( + (deals_per_month - LIQUIDITY_MODERATE_THRESHOLD) + / (LIQUIDITY_HIGH_THRESHOLD - LIQUIDITY_MODERATE_THRESHOLD) + ) + * 25 + ) liquidity_rating = "moderate" elif deals_per_month >= LIQUIDITY_LOW_THRESHOLD: - velocity_score = 25 + ((deals_per_month - LIQUIDITY_LOW_THRESHOLD) / (LIQUIDITY_MODERATE_THRESHOLD - LIQUIDITY_LOW_THRESHOLD)) * 25 + velocity_score = ( + 25 + + ( + (deals_per_month - LIQUIDITY_LOW_THRESHOLD) + / (LIQUIDITY_MODERATE_THRESHOLD - LIQUIDITY_LOW_THRESHOLD) + ) + * 25 + ) liquidity_rating = "low" else: velocity_score = deals_per_month * 50 @@ -405,4 +471,5 @@ def get_market_liquidity( avg_deals_per_month=round(deals_per_month, 2), deal_velocity=round(deals_per_month, 2), market_activity_level=liquidity_rating, + trend_direction=trend_direction, ) diff --git a/nadlan_mcp/govmap/models.py b/nadlan_mcp/govmap/models.py index 5f2d558..6a875e8 100644 --- a/nadlan_mcp/govmap/models.py +++ b/nadlan_mcp/govmap/models.py @@ -8,7 +8,8 @@ and type safety throughout the codebase. from datetime import date, datetime from typing import Any, Dict, List, Optional -from pydantic import BaseModel, Field, field_validator, computed_field, ConfigDict + +from pydantic import BaseModel, ConfigDict, Field, computed_field, field_validator class CoordinatePoint(BaseModel): @@ -19,6 +20,7 @@ class CoordinatePoint(BaseModel): longitude: X coordinate in ITM projection (meters) latitude: Y coordinate in ITM projection (meters) """ + longitude: float = Field(..., description="X coordinate in ITM projection (meters)") latitude: float = Field(..., description="Y coordinate in ITM projection (meters)") @@ -36,6 +38,7 @@ class Address(BaseModel): score: Relevance score from autocomplete coordinates: ITM coordinate point """ + text: str = Field(..., description="Full address text") id: str = Field(..., description="Unique address identifier") type: str = Field(..., description="Address type") @@ -55,6 +58,7 @@ class AutocompleteResult(BaseModel): coordinates: Optional coordinate point shape: Original WKT shape string from API """ + text: str id: str type: str @@ -71,6 +75,7 @@ class AutocompleteResponse(BaseModel): results_count: Number of results returned results: List of autocomplete results """ + results_count: int = Field(alias="resultsCount") results: List[AutocompleteResult] = Field(default_factory=list) @@ -102,6 +107,7 @@ class Deal(BaseModel): source_polygon_id: Source polygon ID sourceorder: Source ordering """ + # Required fields objectid: int = Field(..., description="Unique deal identifier") deal_amount: float = Field(..., alias="dealAmount", description="Transaction amount in NIS") @@ -109,30 +115,40 @@ class Deal(BaseModel): # Common optional fields asset_area: Optional[float] = Field(None, alias="assetArea", description="Property area in sqm") - settlement_name_heb: Optional[str] = Field(None, alias="settlementNameHeb", description="City name in Hebrew") - property_type_description: Optional[str] = Field(None, alias="propertyTypeDescription", description="Property type") + settlement_name_heb: Optional[str] = Field( + None, alias="settlementNameHeb", description="City name in Hebrew" + ) + property_type_description: Optional[str] = Field( + None, alias="propertyTypeDescription", description="Property type" + ) neighborhood: Optional[str] = Field(None, description="Neighborhood name") street_name: Optional[str] = Field(None, alias="streetName", description="Street name") house_number: Optional[str] = Field(None, alias="houseNumber", description="House number") # Floor information floor: Optional[str] = Field(None, description="Floor description (may be Hebrew)") - floor_number: Optional[int] = Field(None, alias="floorNumber", description="Numeric floor number") + floor_number: Optional[int] = Field( + None, alias="floorNumber", description="Numeric floor number" + ) # Additional details rooms: Optional[float] = Field(None, description="Number of rooms") # Priority and metadata (added by our system, not from API) - priority: Optional[int] = Field(None, description="Priority for sorting (0=same building, 1=street, 2=neighborhood)") + priority: Optional[int] = Field( + None, description="Priority for sorting (0=same building, 1=street, 2=neighborhood)" + ) # Geometry and internal fields (often not useful for analysis) shape: Optional[str] = Field(None, description="WKT geometry") - source_polygon_id: Optional[str] = Field(None, alias="sourcePolygonId", description="Source polygon ID") + source_polygon_id: Optional[str] = Field( + None, alias="sourcePolygonId", description="Source polygon ID" + ) sourceorder: Optional[int] = Field(None, description="Source ordering") model_config = ConfigDict( populate_by_name=True, # Allow both alias and field name - extra='allow' # Allow extra fields from API that we don't model + extra="allow", # Allow extra fields from API that we don't model ) @computed_field @@ -148,7 +164,7 @@ class Deal(BaseModel): return round(self.deal_amount / self.asset_area, 2) return None - @field_validator('deal_date', mode='before') + @field_validator("deal_date", mode="before") @classmethod def parse_deal_date(cls, v: Any) -> date: """Parse deal date string into a date object.""" @@ -158,8 +174,8 @@ class Deal(BaseModel): return v.date() if isinstance(v, str): # Handle ISO format with optional time and timezone - if 'T' in v: - v = v.split('T')[0] + if "T" in v: + v = v.split("T")[0] try: return date.fromisoformat(v) except ValueError: @@ -179,37 +195,32 @@ class DealStatistics(BaseModel): property_type_distribution: Count by property type date_range: Earliest and latest deal dates """ + total_deals: int = Field(..., description="Total number of deals analyzed") # Price statistics price_statistics: Dict[str, float] = Field( default_factory=dict, - description="Price stats (mean, median, std_dev, min, max, percentiles)" + description="Price stats (mean, median, std_dev, min, max, percentiles)", ) # Area statistics area_statistics: Dict[str, float] = Field( - default_factory=dict, - description="Area stats (mean, median, std_dev, min, max)" + default_factory=dict, description="Area stats (mean, median, std_dev, min, max)" ) # Price per sqm statistics price_per_sqm_statistics: Dict[str, float] = Field( - default_factory=dict, - description="Price/sqm stats (mean, median, std_dev, min, max)" + default_factory=dict, description="Price/sqm stats (mean, median, std_dev, min, max)" ) # Distribution by property type property_type_distribution: Dict[str, int] = Field( - default_factory=dict, - description="Count of deals by property type" + default_factory=dict, description="Count of deals by property type" ) # Date range - date_range: Optional[Dict[str, str]] = Field( - None, - description="Earliest and latest deal dates" - ) + date_range: Optional[Dict[str, str]] = Field(None, description="Earliest and latest deal dates") class MarketActivityScore(BaseModel): @@ -224,14 +235,16 @@ class MarketActivityScore(BaseModel): time_period_months: Analysis period in months monthly_distribution: Deals per month breakdown """ + activity_score: float = Field(..., description="Overall activity score (0-100)", ge=0, le=100) total_deals: int = Field(..., description="Total deals in period") deals_per_month: float = Field(..., description="Average deals per month") trend: str = Field(..., description="Market trend (increasing, stable, decreasing)") - time_period_months: Optional[int] = Field(None, description="Analysis period in months (None = all data)") + time_period_months: Optional[int] = Field( + None, description="Analysis period in months (None = all data)" + ) monthly_distribution: Dict[str, int] = Field( - default_factory=dict, - description="Deals per month (YYYY-MM: count)" + default_factory=dict, description="Deals per month (YYYY-MM: count)" ) @@ -250,7 +263,10 @@ class InvestmentAnalysis(BaseModel): total_deals: Total deals analyzed (sample size) data_quality: Data quality assessment """ - investment_score: float = Field(..., description="Overall investment score (0-100)", ge=0, le=100) + + investment_score: float = Field( + ..., description="Overall investment score (0-100)", ge=0, le=100 + ) price_trend: str = Field(..., description="Price trend (increasing, stable, decreasing)") price_appreciation_rate: float = Field(..., description="Annual price growth rate (%)") price_volatility: float = Field(..., description="Price volatility score (0-100)", ge=0, le=100) @@ -273,12 +289,17 @@ class LiquidityMetrics(BaseModel): liquidity_rating: Market liquidity rating trend_direction: Liquidity trend direction """ + liquidity_score: float = Field(..., description="Overall liquidity score (0-100)", ge=0, le=100) total_deals: int = Field(..., description="Total deals in period") - time_period_months: Optional[int] = Field(None, description="Analysis period in months (None = all data)") + time_period_months: Optional[int] = Field( + None, description="Analysis period in months (None = all data)" + ) avg_deals_per_month: float = Field(..., description="Average deals per month") deal_velocity: float = Field(..., description="Deal velocity (deals per month)") - market_activity_level: str = Field(..., description="Activity level (very_high, high, moderate, low, very_low)") + market_activity_level: str = Field( + ..., description="Activity level (very_high, high, moderate, low, very_low)" + ) class DealFilters(BaseModel): @@ -298,6 +319,7 @@ class DealFilters(BaseModel): min_floor: Minimum floor number max_floor: Maximum floor number """ + property_type: Optional[str] = Field(None, description="Property type filter") min_rooms: Optional[float] = Field(None, description="Minimum rooms", ge=0) max_rooms: Optional[float] = Field(None, description="Maximum rooms", ge=0) @@ -308,38 +330,38 @@ class DealFilters(BaseModel): min_floor: Optional[int] = Field(None, description="Minimum floor") max_floor: Optional[int] = Field(None, description="Maximum floor") - @field_validator('max_rooms') + @field_validator("max_rooms") @classmethod def validate_max_rooms(cls, v: Optional[float], info) -> Optional[float]: """Ensure max_rooms >= min_rooms if both specified.""" - if v is not None and info.data.get('min_rooms') is not None: - if v < info.data['min_rooms']: + if v is not None and info.data.get("min_rooms") is not None: + if v < info.data["min_rooms"]: raise ValueError("max_rooms must be >= min_rooms") return v - @field_validator('max_price') + @field_validator("max_price") @classmethod def validate_max_price(cls, v: Optional[float], info) -> Optional[float]: """Ensure max_price >= min_price if both specified.""" - if v is not None and info.data.get('min_price') is not None: - if v < info.data['min_price']: + if v is not None and info.data.get("min_price") is not None: + if v < info.data["min_price"]: raise ValueError("max_price must be >= min_price") return v - @field_validator('max_area') + @field_validator("max_area") @classmethod def validate_max_area(cls, v: Optional[float], info) -> Optional[float]: """Ensure max_area >= min_area if both specified.""" - if v is not None and info.data.get('min_area') is not None: - if v < info.data['min_area']: + if v is not None and info.data.get("min_area") is not None: + if v < info.data["min_area"]: raise ValueError("max_area must be >= min_area") return v - @field_validator('max_floor') + @field_validator("max_floor") @classmethod def validate_max_floor(cls, v: Optional[int], info) -> Optional[int]: """Ensure max_floor >= min_floor if both specified.""" - if v is not None and info.data.get('min_floor') is not None: - if v < info.data['min_floor']: + if v is not None and info.data.get("min_floor") is not None: + if v < info.data["min_floor"]: raise ValueError("max_floor must be >= min_floor") return v diff --git a/nadlan_mcp/govmap/statistics.py b/nadlan_mcp/govmap/statistics.py index a128657..326a7ec 100644 --- a/nadlan_mcp/govmap/statistics.py +++ b/nadlan_mcp/govmap/statistics.py @@ -5,9 +5,8 @@ This module provides pure mathematical functions for analyzing real estate deal """ from collections import Counter -from typing import List import logging -from datetime import date +from typing import List from .models import Deal, DealStatistics @@ -78,7 +77,11 @@ def calculate_deal_statistics(deals: List[Deal]) -> DealStatistics: sorted_prices = sorted(prices) price_stats = { "mean": round(sum(prices) / len(prices), 2), - "median": (sorted_prices[len(sorted_prices) // 2] + sorted_prices[(len(sorted_prices) - 1) // 2]) / 2, + "median": ( + sorted_prices[len(sorted_prices) // 2] + + sorted_prices[(len(sorted_prices) - 1) // 2] + ) + / 2, "min": min(prices), "max": max(prices), "p25": sorted_prices[len(sorted_prices) // 4], @@ -123,6 +126,7 @@ def calculate_deal_statistics(deals: List[Deal]) -> DealStatistics: try: # Convert dates to ISO strings for consistent formatting from datetime import date as date_type + parsed_dates = [] for d in deal_dates: try: @@ -133,8 +137,8 @@ def calculate_deal_statistics(deals: List[Deal]) -> DealStatistics: # Handle string dates date_str = str(d) # Handle ISO format with timezone (e.g., "2025-01-01T00:00:00.000Z") - if 'T' in date_str: - date_str = date_str.split('T')[0] + if "T" in date_str: + date_str = date_str.split("T")[0] parsed_dates.append(date_str) except (ValueError, TypeError): logger.warning(f"Invalid date format: {d}") diff --git a/nadlan_mcp/govmap/utils.py b/nadlan_mcp/govmap/utils.py index c49c4d1..6c2181e 100644 --- a/nadlan_mcp/govmap/utils.py +++ b/nadlan_mcp/govmap/utils.py @@ -51,10 +51,7 @@ def is_same_building(search_address: str, deal_address: str) -> bool: """Extract street name and number from address""" # Remove common prefixes/suffixes and normalize addr_clean = ( - addr.replace("רח'", "") - .replace("רחוב", "") - .replace("שד'", "") - .replace("שדרות", "") + addr.replace("רח'", "").replace("רחוב", "").replace("שד'", "").replace("שדרות", "") ) addr_clean = addr_clean.replace(" ", " ").strip() diff --git a/nadlan_mcp/govmap/validators.py b/nadlan_mcp/govmap/validators.py index 7615e5b..d978670 100644 --- a/nadlan_mcp/govmap/validators.py +++ b/nadlan_mcp/govmap/validators.py @@ -57,16 +57,18 @@ def validate_coordinates(point: Tuple[float, float]) -> Tuple[float, float]: # Basic validation for Israeli coordinates (ITM projection) # ITM bounds for Israel: X (longitude) ~150,000-300,000, Y (latitude) ~3,500,000-4,000,000 if not (150000 <= lon <= 300000): # ITM longitude bounds for Israel - logger.warning(f"Longitude {lon} appears to be outside Israeli ITM bounds (150,000-300,000)") + logger.warning( + f"Longitude {lon} appears to be outside Israeli ITM bounds (150,000-300,000)" + ) if not (3500000 <= lat <= 4000000): # ITM latitude bounds for Israel - logger.warning(f"Latitude {lat} appears to be outside Israeli ITM bounds (3,500,000-4,000,000)") + logger.warning( + f"Latitude {lat} appears to be outside Israeli ITM bounds (3,500,000-4,000,000)" + ) return (lon, lat) -def validate_positive_int( - value: int, name: str, max_value: Optional[int] = None -) -> int: +def validate_positive_int(value: int, name: str, max_value: Optional[int] = None) -> int: """ Validate positive integer input. diff --git a/nadlan_mcp/main.py b/nadlan_mcp/main.py index 18a461e..37095e0 100644 --- a/nadlan_mcp/main.py +++ b/nadlan_mcp/main.py @@ -15,40 +15,48 @@ def main(): """ # Initialize client client = GovmapClient() - + # Example address search address = "סוקולוב 38 חולון" - + try: # Find recent deals for address deals = client.find_recent_deals_for_address(address, years_back=2) - + print(f"Found {len(deals)} deals for address: {address}") - + # Display first few deals for i, deal in enumerate(deals[:5]): - print(f"\nDeal {i+1}:") - + print(f"\nDeal {i + 1}:") + # Build address from available fields address_parts = [] - if deal.get('streetNameHeb'): - address_parts.append(deal.get('streetNameHeb')) - if deal.get('houseNum'): - address_parts.append(str(deal.get('houseNum'))) - if deal.get('settlementNameHeb'): - address_parts.append(deal.get('settlementNameHeb')) - address = ' '.join(address_parts) if address_parts else 'N/A' - + if deal.get("streetNameHeb"): + address_parts.append(deal.get("streetNameHeb")) + if deal.get("houseNum"): + address_parts.append(str(deal.get("houseNum"))) + if deal.get("settlementNameHeb"): + address_parts.append(deal.get("settlementNameHeb")) + address = " ".join(address_parts) if address_parts else "N/A" + print(f" Address: {address}") print(f" Date: {deal.get('dealDate', 'N/A')[:10] if deal.get('dealDate') else 'N/A'}") - print(f" Price: {deal.get('dealAmount', 'N/A'):,} NIS" if deal.get('dealAmount') else " Price: N/A") - print(f" Area: {deal.get('assetArea', 'N/A')} m²" if deal.get('assetArea') else " Area: N/A") + print( + f" Price: {deal.get('dealAmount', 'N/A'):,} NIS" + if deal.get("dealAmount") + else " Price: N/A" + ) + print( + f" Area: {deal.get('assetArea', 'N/A')} m²" + if deal.get("assetArea") + else " Area: N/A" + ) print(f" Type: {deal.get('propertyTypeDescription', 'N/A')}") print(f" Neighborhood: {deal.get('neighborhood', 'N/A')}") - + except Exception as e: print(f"Error: {e}") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/pytest.ini b/pytest.ini index 84a8c4d..6e6f5b5 100644 --- a/pytest.ini +++ b/pytest.ini @@ -7,4 +7,4 @@ addopts = -v --tb=short markers = integration: integration tests that make real API calls unit: unit tests with mocked dependencies - api_health: weekly API health check tests (real API calls, run with: pytest -m api_health) \ No newline at end of file + api_health: weekly API health check tests (real API calls, run with: pytest -m api_health) diff --git a/run_fastmcp_server.py b/run_fastmcp_server.py index f5aeb53..42a5ea8 100644 --- a/run_fastmcp_server.py +++ b/run_fastmcp_server.py @@ -7,4 +7,5 @@ This script runs the FastMCP server for accessing Israeli government real estate if __name__ == "__main__": from nadlan_mcp.fastmcp_server import mcp - mcp.run() \ No newline at end of file + + mcp.run() diff --git a/start.prompt b/start.prompt index da6c268..916b961 100644 --- a/start.prompt +++ b/start.prompt @@ -25,15 +25,15 @@ Make sure to the keep most of the parameters available in the functions you crea Here are the query and response examples: ➜ nadlan-mcp git:(main) curl -X POST https://www.govmap.gov.il/api/search-service/autocomplete -H "Content-Type: application/json" -d '{"searchText": "סוקולוב 38 חולון", "language": "he", "isAccurate": false, "maxResults": 10}' -{"resultsCount":339,"results":[{"id":"address|ADDR|346115","text":"סוקולוב 38 חולון","type":"address","score":3655.2979,"shape":"POINT(3870928.84417173 3766290.190868268)","data":{}}],"aggregations":[{"key":"street","count":1},{"key":"address","count":150}]}% +{"resultsCount":339,"results":[{"id":"address|ADDR|346115","text":"סוקולוב 38 חולון","type":"address","score":3655.2979,"shape":"POINT(3870928.84417173 3766290.190868268)","data":{}}],"aggregations":[{"key":"street","count":1},{"key":"address","count":150}]}% ➜ nadlan-mcp git:(main) curl -X POST https://www.govmap.gov.il/api/layers-catalog/entitiesByPoint -H "Content-Type: application/json" -d '{"point":[3870923.9531396534,3766288.069885358],"layers":[{"layerId":"16"}],"tolerance":0}' -{"data":[{"name":"nadlan","caption":"עסקאות נדל\"ן","fieldsMapping":{"parcel":"חלקה","dealtype":"סוג עסקה","gush_num":"גוש","labelname":"שם","locality_name":"ישוב"},"entities":[{"objectId":314375,"centroid":[3870922.194567065,3766289.5137165817],"geom":"MULTIPOLYGON(((3870932.259157911 3766268.605101729,3870922.8279753854 3766269.7513144827,3870923.9715752467 3766278.4940995267,3870919.3620279552 3766279.067629365,3870918.3456935785 3766270.9173726696,3870911.7317985105 3766271.7907086676,3870915.056625225 3766297.9949837825,3870931.208021703 3766295.9166542697,3870929.609220784 3766283.110778894,3870933.9713238077 3766282.5362526667,3870932.259157911 3766268.605101729)))","fields":[{"fieldName":"גוש","fieldValue":"","fieldType":2,"isVisible":true},{"fieldName":"חלקה","fieldValue":"","fieldType":2,"isVisible":true},{"fieldName":"ישוב","fieldValue":"","fieldType":1,"isVisible":true},{"fieldName":"שם","fieldValue":"","fieldType":1,"isVisible":true},{"fieldName":"סוג עסקה","fieldValue":"","fieldType":1,"isVisible":true}]}]}]}% +{"data":[{"name":"nadlan","caption":"עסקאות נדל\"ן","fieldsMapping":{"parcel":"חלקה","dealtype":"סוג עסקה","gush_num":"גוש","labelname":"שם","locality_name":"ישוב"},"entities":[{"objectId":314375,"centroid":[3870922.194567065,3766289.5137165817],"geom":"MULTIPOLYGON(((3870932.259157911 3766268.605101729,3870922.8279753854 3766269.7513144827,3870923.9715752467 3766278.4940995267,3870919.3620279552 3766279.067629365,3870918.3456935785 3766270.9173726696,3870911.7317985105 3766271.7907086676,3870915.056625225 3766297.9949837825,3870931.208021703 3766295.9166542697,3870929.609220784 3766283.110778894,3870933.9713238077 3766282.5362526667,3870932.259157911 3766268.605101729)))","fields":[{"fieldName":"גוש","fieldValue":"","fieldType":2,"isVisible":true},{"fieldName":"חלקה","fieldValue":"","fieldType":2,"isVisible":true},{"fieldName":"ישוב","fieldValue":"","fieldType":1,"isVisible":true},{"fieldName":"שם","fieldValue":"","fieldType":1,"isVisible":true},{"fieldName":"סוג עסקה","fieldValue":"","fieldType":1,"isVisible":true}]}]}]}% ➜ nadlan-mcp git:(main) curl -X GET https://www.govmap.gov.il/api/real-estate/deals/3872355.023513328,3765591.163209798/30 -[{"dealscount":"2","settlementNameHeb":"חולון","streetNameHeb":"גאולים","houseNum":18,"polygon_id":"52272383","objectid":315688},{"dealscount":"1","settlementNameHeb":"חולון","streetNameHeb":"גאולים","houseNum":16,"polygon_id":"52278510","objectid":319592},{"dealscount":"2","settlementNameHeb":"חולון","streetNameHeb":"הקונגרס","houseNum":28,"polygon_id":"52278510","objectid":319592},{"dealscount":"1","settlementNameHeb":"חולון","streetNameHeb":"הקונגרס","houseNum":26,"polygon_id":"52514765","objectid":360560}]% 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"https://www.govmap.gov.il/api/real-estate/neighborhood-deals/52282030?limit=8" -Please ensure the final output is a complete, runnable, and well-documented Python project that fulfills all these requirements. Thank you! \ No newline at end of file +Please ensure the final output is a complete, runnable, and well-documented Python project that fulfills all these requirements. Thank you! diff --git a/tests/__init__.py b/tests/__init__.py index da202f3..6523fc4 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1 +1 @@ -# Tests package for nadlan_mcp \ No newline at end of file +# Tests package for nadlan_mcp diff --git a/tests/api_health/test_govmap_api_health.py b/tests/api_health/test_govmap_api_health.py index 43be92e..1651ca6 100644 --- a/tests/api_health/test_govmap_api_health.py +++ b/tests/api_health/test_govmap_api_health.py @@ -8,8 +8,9 @@ IMPORTANT: These make real API calls and take longer to run. """ import pytest + from nadlan_mcp.govmap import GovmapClient -from nadlan_mcp.govmap.models import Deal, AutocompleteResponse +from nadlan_mcp.govmap.models import AutocompleteResponse, Deal @pytest.fixture @@ -37,16 +38,16 @@ class TestAutocompleteAPIHealth: response = client.autocomplete_address("דיזנגוף תל אביב") # Check response model fields exist - assert hasattr(response, 'results_count') - assert hasattr(response, 'results') + assert hasattr(response, "results_count") + assert hasattr(response, "results") # Check result fields if len(response.results) > 0: result = response.results[0] - assert hasattr(result, 'id') - assert hasattr(result, 'text') - assert hasattr(result, 'type') - assert hasattr(result, 'coordinates') + assert hasattr(result, "id") + assert hasattr(result, "text") + assert hasattr(result, "type") + assert hasattr(result, "coordinates") @pytest.mark.api_health def test_autocomplete_coordinates_present(self, client): @@ -128,18 +129,18 @@ class TestDealsAPIHealth: deal = deals[0] # Check required fields - assert hasattr(deal, 'objectid') - assert hasattr(deal, 'deal_amount') - assert hasattr(deal, 'deal_date') + assert hasattr(deal, "objectid") + assert hasattr(deal, "deal_amount") + assert hasattr(deal, "deal_date") # Check common optional fields - assert hasattr(deal, 'asset_area') - assert hasattr(deal, 'property_type_description') - assert hasattr(deal, 'rooms') - assert hasattr(deal, 'floor') + assert hasattr(deal, "asset_area") + assert hasattr(deal, "property_type_description") + assert hasattr(deal, "rooms") + assert hasattr(deal, "floor") # Check computed field - assert hasattr(deal, 'price_per_sqm') + assert hasattr(deal, "price_per_sqm") class TestAPIDataQuality: @@ -169,8 +170,9 @@ class TestAPIDataQuality: # Check deal amounts are reasonable (10K to 100M NIS) for deal in deals: if deal.deal_amount > 0: - assert 10000 <= deal.deal_amount <= 100000000, \ - f"Deal amount {deal.deal_amount} outside reasonable range" + assert ( + 10000 <= deal.deal_amount <= 100000000 + ), f"Deal amount {deal.deal_amount} outside reasonable range" @pytest.mark.api_health def test_dates_are_recent(self, client): @@ -196,7 +198,7 @@ class TestAPIDataQuality: pytest.skip("No deals") # At least some deals should be from last 5 years - cutoff = date.today() - timedelta(days=5*365) + cutoff = date.today() - timedelta(days=5 * 365) recent_deals = [d for d in deals if isinstance(d.deal_date, date) and d.deal_date >= cutoff] assert len(recent_deals) > 0, "No recent deals found (within last 5 years)" diff --git a/tests/conftest.py b/tests/conftest.py index 3cf2ee4..f80d937 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,8 +2,10 @@ Pytest configuration for nadlan_mcp tests. """ -import pytest from unittest.mock import Mock + +import pytest + from tests.vcr_config import my_vcr @@ -27,9 +29,9 @@ def sample_autocomplete_response(): "type": "address", "score": 100, "shape": "POINT(3870000.123 3770000.456)", - "data": {} + "data": {}, } - ] + ], } @@ -46,7 +48,7 @@ def sample_deals_response(): "assetArea": 100, "settlementNameHeb": "תל אביב-יפו", "propertyTypeDescription": "דירה", - "neighborhood": "test neighborhood" + "neighborhood": "test neighborhood", }, { "objectid": 456, @@ -55,9 +57,9 @@ def sample_deals_response(): "assetArea": 120, "settlementNameHeb": "תל אביב-יפو", "propertyTypeDescription": "דירה", - "neighborhood": "test neighborhood" - } - ] + "neighborhood": "test neighborhood", + }, + ], } diff --git a/tests/e2e/test_mcp_tools.py b/tests/e2e/test_mcp_tools.py index 6e61f3a..de9b55b 100644 --- a/tests/e2e/test_mcp_tools.py +++ b/tests/e2e/test_mcp_tools.py @@ -6,13 +6,16 @@ For comprehensive E2E testing, see test_mcp_tools_comprehensive.py Target: Complete in <30 seconds """ + import json + import pytest + from nadlan_mcp.fastmcp_server import ( autocomplete_address, find_recent_deals_for_address, - get_street_deals, get_deals_by_radius, + get_street_deals, ) @@ -58,10 +61,7 @@ class TestMCPToolsSmokeTests: """Smoke test: Main tool works with minimal data.""" # Use very small limits to speed up result = find_recent_deals_for_address( - self.TEST_ADDRESS, - years_back=1, - radius_meters=30, - max_deals=10 + self.TEST_ADDRESS, years_back=1, radius_meters=30, max_deals=10 ) data = json.loads(result) diff --git a/tests/e2e/test_mcp_tools_comprehensive.py b/tests/e2e/test_mcp_tools_comprehensive.py index 7bff100..740af36 100644 --- a/tests/e2e/test_mcp_tools_comprehensive.py +++ b/tests/e2e/test_mcp_tools_comprehensive.py @@ -6,19 +6,22 @@ of each MCP tool from end to end. Mark as integration tests since they hit real APIs. """ + import json + import pytest + from nadlan_mcp.fastmcp_server import ( - autocomplete_address, - find_recent_deals_for_address, analyze_market_trends, - get_valuation_comparables, - get_deal_statistics, - get_market_activity_metrics, + autocomplete_address, compare_addresses, - get_street_deals, - get_neighborhood_deals, + find_recent_deals_for_address, + get_deal_statistics, get_deals_by_radius, + get_market_activity_metrics, + get_neighborhood_deals, + get_street_deals, + get_valuation_comparables, ) @@ -69,9 +72,7 @@ class TestMCPToolsE2E: def test_analyze_market_trends(self): """Test market trend analysis.""" - result = analyze_market_trends( - self.TEST_ADDRESS_1, years_back=3, radius_meters=100 - ) + result = analyze_market_trends(self.TEST_ADDRESS_1, years_back=3, radius_meters=100) data = json.loads(result) # Check response structure @@ -83,10 +84,7 @@ class TestMCPToolsE2E: def test_get_valuation_comparables(self): """Test getting valuation comparables.""" result = get_valuation_comparables( - self.TEST_ADDRESS_1, - years_back=3, - min_rooms=3.0, - max_rooms=5.0 + self.TEST_ADDRESS_1, years_back=3, min_rooms=3.0, max_rooms=5.0 ) data = json.loads(result) diff --git a/tests/fixtures/autocomplete_response.json b/tests/fixtures/autocomplete_response.json index 800752d..f1aa4c6 100644 --- a/tests/fixtures/autocomplete_response.json +++ b/tests/fixtures/autocomplete_response.json @@ -32,4 +32,4 @@ }, "shape": "POINT(3871143.6159681133 3766329.319199102)" } -] \ No newline at end of file +] diff --git a/tests/fixtures/neighborhood_deals.json b/tests/fixtures/neighborhood_deals.json index 9d29ce9..b0c0259 100644 --- a/tests/fixtures/neighborhood_deals.json +++ b/tests/fixtures/neighborhood_deals.json @@ -329,4 +329,4 @@ "polygonId": "52507812", "price_per_sqm": 27230.77 } -] \ No newline at end of file +] diff --git a/tests/fixtures/polygon_metadata.json b/tests/fixtures/polygon_metadata.json index 13b74e8..4fd2c75 100644 --- a/tests/fixtures/polygon_metadata.json +++ b/tests/fixtures/polygon_metadata.json @@ -79,4 +79,4 @@ "polygon_id": "7172-7", "objectid": 14315 } -] \ No newline at end of file +] diff --git a/tests/fixtures/street_deals.json b/tests/fixtures/street_deals.json index 4eb2585..40e1093 100644 --- a/tests/fixtures/street_deals.json +++ b/tests/fixtures/street_deals.json @@ -329,4 +329,4 @@ "polygonId": "52385050", "price_per_sqm": 10238.1 } -] \ No newline at end of file +] diff --git a/tests/govmap/test_filters.py b/tests/govmap/test_filters.py index e34ec9d..1633a43 100644 --- a/tests/govmap/test_filters.py +++ b/tests/govmap/test_filters.py @@ -5,6 +5,7 @@ Comprehensive tests for filter_deals_by_criteria function. """ import pytest + from nadlan_mcp.govmap.filters import filter_deals_by_criteria from nadlan_mcp.govmap.models import Deal, DealFilters @@ -80,83 +81,79 @@ class TestFilterDealsByCriteria: result = filter_deals_by_criteria(sample_deals, property_type="דירה") # Should match "דירה" and "דירת גג" via substring match. assert len(result) == 3 - assert set(d.objectid for d in result) == {1, 2, 4} + assert {d.objectid for d in result} == {1, 2, 4} def test_filter_by_min_rooms(self, sample_deals): """Test filtering by minimum rooms.""" result = filter_deals_by_criteria(sample_deals, min_rooms=4.0) assert len(result) == 3 - assert set(d.objectid for d in result) == {2, 3, 5} + assert {d.objectid for d in result} == {2, 3, 5} def test_filter_by_max_rooms(self, sample_deals): """Test filtering by maximum rooms.""" result = filter_deals_by_criteria(sample_deals, max_rooms=3.0) assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 4} + assert {d.objectid for d in result} == {1, 4} def test_filter_by_room_range(self, sample_deals): """Test filtering by room range.""" result = filter_deals_by_criteria(sample_deals, min_rooms=3.0, max_rooms=4.0) assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 2} + assert {d.objectid for d in result} == {1, 2} def test_filter_by_min_price(self, sample_deals): """Test filtering by minimum price.""" result = filter_deals_by_criteria(sample_deals, min_price=1200000.0) assert len(result) == 3 - assert set(d.objectid for d in result) == {2, 3, 5} + assert {d.objectid for d in result} == {2, 3, 5} def test_filter_by_max_price(self, sample_deals): """Test filtering by maximum price.""" result = filter_deals_by_criteria(sample_deals, max_price=1000000.0) assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 4} + assert {d.objectid for d in result} == {1, 4} def test_filter_by_price_range(self, sample_deals): """Test filtering by price range.""" - result = filter_deals_by_criteria( - sample_deals, min_price=1000000.0, max_price=1500000.0 - ) + result = filter_deals_by_criteria(sample_deals, min_price=1000000.0, max_price=1500000.0) assert len(result) == 3 - assert set(d.objectid for d in result) == {1, 2, 5} + assert {d.objectid for d in result} == {1, 2, 5} def test_filter_by_min_area(self, sample_deals): """Test filtering by minimum area.""" result = filter_deals_by_criteria(sample_deals, min_area=100.0) assert len(result) == 3 - assert set(d.objectid for d in result) == {2, 3, 5} + assert {d.objectid for d in result} == {2, 3, 5} def test_filter_by_max_area(self, sample_deals): """Test filtering by maximum area.""" result = filter_deals_by_criteria(sample_deals, max_area=80.0) assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 4} + assert {d.objectid for d in result} == {1, 4} def test_filter_by_area_range(self, sample_deals): """Test filtering by area range.""" - result = filter_deals_by_criteria( - sample_deals, min_area=80.0, max_area=120.0 - ) + result = filter_deals_by_criteria(sample_deals, min_area=80.0, max_area=120.0) assert len(result) == 3 - assert set(d.objectid for d in result) == {1, 2, 5} + assert {d.objectid for d in result} == {1, 2, 5} def test_filter_by_min_floor(self, sample_deals): """Test filtering by minimum floor.""" result = filter_deals_by_criteria(sample_deals, min_floor=2) assert len(result) == 3 - assert set(d.objectid for d in result) == {1, 2, 5} + assert {d.objectid for d in result} == {1, 2, 5} def test_filter_by_max_floor(self, sample_deals): """Test filtering by maximum floor.""" result = filter_deals_by_criteria(sample_deals, max_floor=2) assert len(result) == 3 - assert set(d.objectid for d in result) == {1, 3, 4} + assert {d.objectid for d in result} == {1, 3, 4} def test_filter_by_floor_range(self, sample_deals): """Test filtering by floor range.""" result = filter_deals_by_criteria(sample_deals, min_floor=1, max_floor=5) assert len(result) == 3 - assert set(d.objectid for d in result) == {1, 2, 4} + assert {d.objectid for d in result} == {1, 2, 4} def test_combined_filters(self, sample_deals): """Test combining multiple filters.""" @@ -171,19 +168,17 @@ class TestFilterDealsByCriteria: # objectid=1: "דירה", 3 rooms, 1M price ✓ # objectid=2: "דירת גג" (matches "דירה" variant), 4 rooms, 1.5M price ✓ assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 2} + assert {d.objectid for d in result} == {1, 2} def test_filter_using_dealfilters_model(self, sample_deals): """Test filtering using DealFilters model.""" - filters = DealFilters( - property_type="דירה", min_rooms=3.0, max_rooms=4.0 - ) + filters = DealFilters(property_type="דירה", min_rooms=3.0, max_rooms=4.0) result = filter_deals_by_criteria(sample_deals, filters=filters) # Should match objectid=1 and objectid=2 # objectid=1: "דירה", 3 rooms ✓ # objectid=2: "דירת גג" (matches "דירה" variant), 4 rooms ✓ assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 2} + assert {d.objectid for d in result} == {1, 2} def test_filter_using_dict(self, sample_deals): """Test filtering using dict (converted to DealFilters).""" @@ -197,23 +192,35 @@ class TestFilterDealsByCriteria: # objectid=1: "דירה", 3 rooms ✓ # objectid=2: "דירת גג" (matches "דירה" variant), 4 rooms ✓ assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 2} + assert {d.objectid for d in result} == {1, 2} def test_individual_params_override_filters_model(self, sample_deals): """Test that individual parameters override filters model.""" filters = DealFilters(min_rooms=5.0) # Would match only objectid=3 result = filter_deals_by_criteria( - sample_deals, filters=filters, min_rooms=3.0 # Override to 3.0 + sample_deals, + filters=filters, + min_rooms=3.0, # Override to 3.0 ) # Should use min_rooms=3.0, not 5.0 assert len(result) == 4 - assert set(d.objectid for d in result) == {1, 2, 3, 5} + assert {d.objectid for d in result} == {1, 2, 3, 5} def test_filters_exclude_deals_with_missing_property_type(self): """Test that deals with missing property_type are excluded when filter active.""" deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date="2024-01-01", property_type_description="דירה"), - Deal(objectid=2, deal_amount=1000000, deal_date="2024-01-01", property_type_description=None), + Deal( + objectid=1, + deal_amount=1000000, + deal_date="2024-01-01", + property_type_description="דירה", + ), + Deal( + objectid=2, + deal_amount=1000000, + deal_date="2024-01-01", + property_type_description=None, + ), Deal(objectid=3, deal_amount=1000000, deal_date="2024-01-01"), # No property_type ] result = filter_deals_by_criteria(deals, property_type="דירה") @@ -281,13 +288,17 @@ class TestFilterDealsByCriteria: """Test floor filtering with Hebrew floor descriptions.""" deals = [ Deal(objectid=1, deal_amount=1000000, deal_date="2024-01-01", floor="קרקע"), # Ground=0 - Deal(objectid=2, deal_amount=1000000, deal_date="2024-01-01", floor="קומה 3"), # Floor 3 - Deal(objectid=3, deal_amount=1000000, deal_date="2024-01-01", floor="מרתף"), # Basement=-1 + Deal( + objectid=2, deal_amount=1000000, deal_date="2024-01-01", floor="קומה 3" + ), # Floor 3 + Deal( + objectid=3, deal_amount=1000000, deal_date="2024-01-01", floor="מרתף" + ), # Basement=-1 ] result = filter_deals_by_criteria(deals, min_floor=0) # Should match ground (0) and floor 3, but not basement (-1) assert len(result) == 2 - assert set(d.objectid for d in result) == {1, 2} + assert {d.objectid for d in result} == {1, 2} @pytest.mark.parametrize( "min_val,max_val,expected_count", diff --git a/tests/govmap/test_market_analysis.py b/tests/govmap/test_market_analysis.py index bced8c7..87f2e51 100644 --- a/tests/govmap/test_market_analysis.py +++ b/tests/govmap/test_market_analysis.py @@ -4,15 +4,17 @@ Tests for nadlan_mcp.govmap.market_analysis module. Comprehensive tests for market analysis functions. """ +from datetime import datetime, timedelta + import pytest -from datetime import date, datetime, timedelta + from nadlan_mcp.govmap.market_analysis import ( - parse_deal_dates, - calculate_market_activity_score, analyze_investment_potential, + calculate_market_activity_score, get_market_liquidity, + parse_deal_dates, ) -from nadlan_mcp.govmap.models import Deal, MarketActivityScore, InvestmentAnalysis, LiquidityMetrics +from nadlan_mcp.govmap.models import Deal, InvestmentAnalysis, LiquidityMetrics, MarketActivityScore def get_recent_date(months_ago=0, days_ago=0): @@ -27,11 +29,36 @@ class TestParseDealDates: def sample_deals(self): """Create sample deals spanning multiple months (recent dates).""" return [ - Deal(objectid=1, deal_amount=1000000, deal_date=get_recent_date(months_ago=4, days_ago=15), asset_area=80), - Deal(objectid=2, deal_amount=1100000, deal_date=get_recent_date(months_ago=4, days_ago=10), asset_area=85), - Deal(objectid=3, deal_amount=1200000, deal_date=get_recent_date(months_ago=3, days_ago=20), asset_area=90), - Deal(objectid=4, deal_amount=1300000, deal_date=get_recent_date(months_ago=2, days_ago=25), asset_area=95), - Deal(objectid=5, deal_amount=1400000, deal_date=get_recent_date(months_ago=1, days_ago=18), asset_area=100), + Deal( + objectid=1, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=4, days_ago=15), + asset_area=80, + ), + Deal( + objectid=2, + deal_amount=1100000, + deal_date=get_recent_date(months_ago=4, days_ago=10), + asset_area=85, + ), + Deal( + objectid=3, + deal_amount=1200000, + deal_date=get_recent_date(months_ago=3, days_ago=20), + asset_area=90, + ), + Deal( + objectid=4, + deal_amount=1300000, + deal_date=get_recent_date(months_ago=2, days_ago=25), + asset_area=95, + ), + Deal( + objectid=5, + deal_amount=1400000, + deal_date=get_recent_date(months_ago=1, days_ago=18), + asset_area=100, + ), ] def test_parse_deal_dates_basic(self, sample_deals): @@ -60,8 +87,18 @@ class TestParseDealDates: """Test parsing with date objects instead of strings.""" recent = datetime.now().date() deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date=recent - timedelta(days=30), asset_area=80), - Deal(objectid=2, deal_amount=1100000, deal_date=recent - timedelta(days=60), asset_area=85), + Deal( + objectid=1, + deal_amount=1000000, + deal_date=recent - timedelta(days=30), + asset_area=80, + ), + Deal( + objectid=2, + deal_amount=1100000, + deal_date=recent - timedelta(days=60), + asset_area=85, + ), ] deal_dates, monthly, _ = parse_deal_dates(deals) @@ -71,8 +108,18 @@ class TestParseDealDates: def test_parse_deal_dates_all_valid(self): """Test that all valid dates are parsed.""" deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date=get_recent_date(months_ago=1), asset_area=80), - Deal(objectid=2, deal_amount=1100000, deal_date=get_recent_date(months_ago=2), asset_area=85), + Deal( + objectid=1, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=1), + asset_area=80, + ), + Deal( + objectid=2, + deal_amount=1100000, + deal_date=get_recent_date(months_ago=2), + asset_area=85, + ), ] deal_dates, _, _ = parse_deal_dates(deals) assert len(deal_dates) == 2 @@ -90,7 +137,12 @@ class TestCalculateMarketActivityScore: """Test basic market activity calculation.""" # Create 12 deals spread across last 12 months deals = [ - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=i), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=i), + asset_area=80, + ) for i in range(12) ] score = calculate_market_activity_score(deals, time_period_months=12) @@ -106,10 +158,15 @@ class TestCalculateMarketActivityScore: # Create 120 deals spread across last 10 months = ~12 deals/month (very high) deals = [] for i in range(120): - month_ago = (i % 10) + month_ago = i % 10 day = (i % 28) + 1 deals.append( - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=month_ago, days_ago=day), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=month_ago, days_ago=day), + asset_area=80, + ) ) score = calculate_market_activity_score(deals, time_period_months=12) @@ -118,8 +175,18 @@ class TestCalculateMarketActivityScore: def test_market_activity_low_volume(self): """Test activity score with low volume.""" deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date=get_recent_date(months_ago=1), asset_area=80), - Deal(objectid=2, deal_amount=1100000, deal_date=get_recent_date(months_ago=6), asset_area=85), + Deal( + objectid=1, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=1), + asset_area=80, + ), + Deal( + objectid=2, + deal_amount=1100000, + deal_date=get_recent_date(months_ago=6), + asset_area=85, + ), ] score = calculate_market_activity_score(deals, time_period_months=12) @@ -135,7 +202,12 @@ class TestCalculateMarketActivityScore: num_deals = i + 1 # Increasing: 1 deal earliest, 12 deals most recent for j in range(num_deals): deals.append( - Deal(objectid=len(deals), deal_amount=1000000, deal_date=get_recent_date(months_ago=months_ago, days_ago=j), asset_area=80) + Deal( + objectid=len(deals), + deal_amount=1000000, + deal_date=get_recent_date(months_ago=months_ago, days_ago=j), + asset_area=80, + ) ) score = calculate_market_activity_score(deals, time_period_months=12) @@ -150,7 +222,12 @@ class TestCalculateMarketActivityScore: num_deals = 12 - i # Decreasing: 12 deals earliest, 1 deal most recent for j in range(num_deals): deals.append( - Deal(objectid=len(deals), deal_amount=1000000, deal_date=get_recent_date(months_ago=months_ago, days_ago=j), asset_area=80) + Deal( + objectid=len(deals), + deal_amount=1000000, + deal_date=get_recent_date(months_ago=months_ago, days_ago=j), + asset_area=80, + ) ) score = calculate_market_activity_score(deals, time_period_months=12) @@ -160,7 +237,12 @@ class TestCalculateMarketActivityScore: """Test trend detection - stable activity.""" # Same number of deals each month deals = [ - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=i), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=i), + asset_area=80, + ) for i in range(12) ] score = calculate_market_activity_score(deals, time_period_months=12) @@ -171,8 +253,18 @@ class TestCalculateMarketActivityScore: def test_market_activity_insufficient_data_for_trend(self): """Test trend with insufficient data.""" deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date=get_recent_date(months_ago=1), asset_area=80), - Deal(objectid=2, deal_amount=1100000, deal_date=get_recent_date(months_ago=2), asset_area=85), + Deal( + objectid=1, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=1), + asset_area=80, + ), + Deal( + objectid=2, + deal_amount=1100000, + deal_date=get_recent_date(months_ago=2), + asset_area=85, + ), ] score = calculate_market_activity_score(deals, time_period_months=12) @@ -198,7 +290,12 @@ class TestCalculateMarketActivityScore: month_ago = i % months day = (i // months) % 28 deals.append( - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=month_ago, days_ago=day), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=month_ago, days_ago=day), + asset_area=80, + ) ) score = calculate_market_activity_score(deals, time_period_months=12) @@ -212,9 +309,15 @@ class TestAnalyzeInvestmentPotential: """Test basic investment analysis.""" # Create deals with increasing prices deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date="2024-01-01", asset_area=100), # 10000/sqm - Deal(objectid=2, deal_amount=1100000, deal_date="2024-02-01", asset_area=100), # 11000/sqm - Deal(objectid=3, deal_amount=1200000, deal_date="2024-03-01", asset_area=100), # 12000/sqm + Deal( + objectid=1, deal_amount=1000000, deal_date="2024-01-01", asset_area=100 + ), # 10000/sqm + Deal( + objectid=2, deal_amount=1100000, deal_date="2024-02-01", asset_area=100 + ), # 11000/sqm + Deal( + objectid=3, deal_amount=1200000, deal_date="2024-03-01", asset_area=100 + ), # 12000/sqm ] analysis = analyze_investment_potential(deals) @@ -253,7 +356,12 @@ class TestAnalyzeInvestmentPotential: """Test volatility calculation with low volatility.""" # Prices very similar deals = [ - Deal(objectid=i, deal_amount=1000000 + i*1000, deal_date=f"2024-{i+1:02d}-01", asset_area=100) + Deal( + objectid=i, + deal_amount=1000000 + i * 1000, + deal_date=f"2024-{i + 1:02d}-01", + asset_area=100, + ) for i in range(5) ] analysis = analyze_investment_potential(deals) @@ -278,7 +386,12 @@ class TestAnalyzeInvestmentPotential: def test_investment_analysis_data_quality_excellent(self): """Test data quality assessment with excellent data.""" deals = [ - Deal(objectid=i, deal_amount=1000000, deal_date=f"2024-{(i%12)+1:02d}-01", asset_area=100) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=f"2024-{(i % 12) + 1:02d}-01", + asset_area=100, + ) for i in range(25) ] analysis = analyze_investment_potential(deals) @@ -334,7 +447,7 @@ class TestAnalyzeInvestmentPotential: def test_investment_analysis_price_trends(self, price_changes, expected_trend): """Parametrized test for price trend detection.""" deals = [ - Deal(objectid=i, deal_amount=price, deal_date=f"2024-{i+1:02d}-01", asset_area=100) + Deal(objectid=i, deal_amount=price, deal_date=f"2024-{i + 1:02d}-01", asset_area=100) for i, price in enumerate(price_changes) ] analysis = analyze_investment_potential(deals) @@ -348,7 +461,12 @@ class TestGetMarketLiquidity: def test_market_liquidity_basic(self): """Test basic liquidity calculation.""" deals = [ - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=i), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=i), + asset_area=80, + ) for i in range(12) ] liquidity = get_market_liquidity(deals, time_period_months=12) @@ -367,7 +485,12 @@ class TestGetMarketLiquidity: month_ago = i % 10 day = (i % 28) + 1 deals.append( - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=month_ago, days_ago=day), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=month_ago, days_ago=day), + asset_area=80, + ) ) liquidity = get_market_liquidity(deals, time_period_months=12) @@ -377,8 +500,18 @@ class TestGetMarketLiquidity: def test_market_liquidity_low(self): """Test low liquidity.""" deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date=get_recent_date(months_ago=1), asset_area=80), - Deal(objectid=2, deal_amount=1100000, deal_date=get_recent_date(months_ago=6), asset_area=85), + Deal( + objectid=1, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=1), + asset_area=80, + ), + Deal( + objectid=2, + deal_amount=1100000, + deal_date=get_recent_date(months_ago=6), + asset_area=85, + ), ] liquidity = get_market_liquidity(deals, time_period_months=12) @@ -389,7 +522,12 @@ class TestGetMarketLiquidity: """Test deal velocity calculation.""" # 12 deals spread evenly across 12 months deals = [ - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=i), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=i), + asset_area=80, + ) for i in range(12) ] liquidity = get_market_liquidity(deals, time_period_months=12) @@ -420,7 +558,12 @@ class TestGetMarketLiquidity: month_ago = i % months day = (i // months) % 28 deals.append( - Deal(objectid=i, deal_amount=1000000, deal_date=get_recent_date(months_ago=month_ago, days_ago=day), asset_area=80) + Deal( + objectid=i, + deal_amount=1000000, + deal_date=get_recent_date(months_ago=month_ago, days_ago=day), + asset_area=80, + ) ) liquidity = get_market_liquidity(deals, time_period_months=12) diff --git a/tests/govmap/test_models.py b/tests/govmap/test_models.py index 05db86a..f5781b7 100644 --- a/tests/govmap/test_models.py +++ b/tests/govmap/test_models.py @@ -5,21 +5,20 @@ This module tests all Pydantic model validation, serialization, and computed fields to ensure type safety and correctness. """ -import pytest -from datetime import datetime from pydantic import ValidationError +import pytest from nadlan_mcp.govmap.models import ( - CoordinatePoint, Address, - AutocompleteResult, AutocompleteResponse, + AutocompleteResult, + CoordinatePoint, Deal, + DealFilters, DealStatistics, - MarketActivityScore, InvestmentAnalysis, LiquidityMetrics, - DealFilters, + MarketActivityScore, ) @@ -51,11 +50,7 @@ class TestAddress: """Test creating valid address.""" coord = CoordinatePoint(longitude=180000.0, latitude=650000.0) address = Address( - text="סוקולוב 38 חולון", - id="addr123", - type="address", - score=95.5, - coordinates=coord + text="סוקולוב 38 חולון", id="addr123", type="address", score=95.5, coordinates=coord ) assert address.text == "סוקולוב 38 חולון" assert address.score == 95.5 @@ -63,11 +58,7 @@ class TestAddress: def test_address_without_coordinates(self): """Test creating address without coordinates.""" - address = Address( - text="סוקולוב 38 חולון", - id="addr123", - type="address" - ) + address = Address(text="סוקולוב 38 חולון", id="addr123", type="address") assert address.coordinates is None assert address.score == 0 # Default value @@ -84,7 +75,7 @@ class TestAutocompleteResult: type="city", score=100.0, coordinates=coord, - shape="POINT(180000.0 650000.0)" + shape="POINT(180000.0 650000.0)", ) assert result.text == "חולון" assert result.coordinates.longitude == 180000.0 @@ -92,11 +83,7 @@ class TestAutocompleteResult: def test_result_without_shape(self): """Test result without shape data.""" - result = AutocompleteResult( - text="חולון", - id="city123", - type="city" - ) + result = AutocompleteResult(text="חולון", id="city123", type="city") assert result.shape is None assert result.coordinates is None @@ -108,7 +95,7 @@ class TestAutocompleteResponse: """Test creating valid autocomplete response.""" results = [ AutocompleteResult(text="חולון", id="city1", type="city"), - AutocompleteResult(text="חולון סוקולוב", id="street1", type="street") + AutocompleteResult(text="חולון סוקולוב", id="street1", type="street"), ] response = AutocompleteResponse(resultsCount=2, results=results) assert response.results_count == 2 @@ -116,10 +103,7 @@ class TestAutocompleteResponse: def test_response_with_alias(self): """Test that camelCase alias works.""" - response = AutocompleteResponse.model_validate({ - "resultsCount": 5, - "results": [] - }) + response = AutocompleteResponse.model_validate({"resultsCount": 5, "results": []}) assert response.results_count == 5 def test_empty_response(self): @@ -143,7 +127,7 @@ class TestDeal: property_type_description="דירה", street_name="סוקולוב", house_number="38", - rooms=3.5 + rooms=3.5, ) assert deal.objectid == 12345 assert deal.deal_amount == 1500000.0 @@ -151,43 +135,31 @@ class TestDeal: def test_deal_with_aliases(self): """Test creating deal using API camelCase field names.""" - deal = Deal.model_validate({ - "objectid": 12345, - "dealAmount": 1500000.0, - "dealDate": "2024-01-15", - "assetArea": 85.0, - "propertyTypeDescription": "דירה" - }) + deal = Deal.model_validate( + { + "objectid": 12345, + "dealAmount": 1500000.0, + "dealDate": "2024-01-15", + "assetArea": 85.0, + "propertyTypeDescription": "דירה", + } + ) assert deal.deal_amount == 1500000.0 assert deal.property_type_description == "דירה" def test_deal_price_per_sqm_computed(self): """Test price_per_sqm computed field.""" - deal = Deal( - objectid=12345, - deal_amount=1500000.0, - deal_date="2024-01-15", - asset_area=85.0 - ) + deal = Deal(objectid=12345, deal_amount=1500000.0, deal_date="2024-01-15", asset_area=85.0) assert deal.price_per_sqm == round(1500000.0 / 85.0, 2) def test_deal_price_per_sqm_no_area(self): """Test price_per_sqm returns None when area is missing.""" - deal = Deal( - objectid=12345, - deal_amount=1500000.0, - deal_date="2024-01-15" - ) + deal = Deal(objectid=12345, deal_amount=1500000.0, deal_date="2024-01-15") assert deal.price_per_sqm is None def test_deal_price_per_sqm_zero_area(self): """Test price_per_sqm returns None when area is zero.""" - deal = Deal( - objectid=12345, - deal_amount=1500000.0, - deal_date="2024-01-15", - asset_area=0.0 - ) + deal = Deal(objectid=12345, deal_amount=1500000.0, deal_date="2024-01-15", asset_area=0.0) assert deal.price_per_sqm is None def test_deal_extra_fields_allowed(self): @@ -197,7 +169,7 @@ class TestDeal: "dealAmount": 1500000.0, "dealDate": "2024-01-15", "extra_field": "extra_value", - "another_field": 123 + "another_field": 123, } deal = Deal.model_validate(deal_data) # Extra fields should be stored @@ -205,12 +177,7 @@ class TestDeal: def test_deal_serialization(self): """Test deal serialization to dict.""" - deal = Deal( - objectid=12345, - deal_amount=1500000.0, - deal_date="2024-01-15", - asset_area=85.0 - ) + deal = Deal(objectid=12345, deal_amount=1500000.0, deal_date="2024-01-15", asset_area=85.0) deal_dict = deal.model_dump() assert deal_dict["objectid"] == 12345 assert deal_dict["deal_amount"] == 1500000.0 @@ -218,11 +185,7 @@ class TestDeal: def test_deal_serialization_exclude_none(self): """Test deal serialization excluding None values.""" - deal = Deal( - objectid=12345, - deal_amount=1500000.0, - deal_date="2024-01-15" - ) + deal = Deal(objectid=12345, deal_amount=1500000.0, deal_date="2024-01-15") deal_dict = deal.model_dump(exclude_none=True) assert "asset_area" not in deal_dict assert "rooms" not in deal_dict @@ -240,28 +203,11 @@ class TestDealStatistics: """Test creating valid deal statistics.""" stats = DealStatistics( total_deals=100, - price_statistics={ - "mean": 1500000.0, - "median": 1400000.0, - "std_dev": 200000.0 - }, - area_statistics={ - "mean": 85.5, - "median": 82.0 - }, - price_per_sqm_statistics={ - "mean": 17500.0, - "median": 17200.0 - }, - property_type_distribution={ - "דירה": 80, - "דירת גן": 15, - "פנטהאוז": 5 - }, - date_range={ - "earliest": "2022-01-01", - "latest": "2024-12-31" - } + price_statistics={"mean": 1500000.0, "median": 1400000.0, "std_dev": 200000.0}, + area_statistics={"mean": 85.5, "median": 82.0}, + price_per_sqm_statistics={"mean": 17500.0, "median": 17200.0}, + property_type_distribution={"דירה": 80, "דירת גן": 15, "פנטהאוז": 5}, + date_range={"earliest": "2022-01-01", "latest": "2024-12-31"}, ) assert stats.total_deals == 100 assert stats.price_statistics["mean"] == 1500000.0 @@ -286,7 +232,7 @@ class TestMarketActivityScore: deals_per_month=10.0, trend="increasing", time_period_months=12, - monthly_distribution={"2024-01": 8, "2024-02": 12} + monthly_distribution={"2024-01": 8, "2024-02": 12}, ) assert score.activity_score == 75.5 assert score.trend == "increasing" @@ -300,7 +246,7 @@ class TestMarketActivityScore: total_deals=100, deals_per_month=8.0, trend="stable", - time_period_months=12 + time_period_months=12, ) with pytest.raises(ValidationError): @@ -309,7 +255,7 @@ class TestMarketActivityScore: total_deals=100, deals_per_month=8.0, trend="stable", - time_period_months=12 + time_period_months=12, ) @@ -327,7 +273,7 @@ class TestInvestmentAnalysis: avg_price_per_sqm=17500.0, price_change_pct=12.5, total_deals=85, - data_quality="good" + data_quality="good", ) assert analysis.investment_score == 68.5 assert analysis.price_trend == "increasing" @@ -345,7 +291,7 @@ class TestInvestmentAnalysis: avg_price_per_sqm=17000.0, price_change_pct=5.0, total_deals=100, - data_quality="excellent" + data_quality="excellent", ) @@ -360,7 +306,7 @@ class TestLiquidityMetrics: time_period_months=12, avg_deals_per_month=12.5, deal_velocity=12.5, - market_activity_level="high" + market_activity_level="high", ) assert metrics.liquidity_score == 82.3 assert metrics.market_activity_level == "high" @@ -381,7 +327,7 @@ class TestDealFilters: min_area=60.0, max_area=100.0, min_floor=1, - max_floor=5 + max_floor=5, ) assert filters.property_type == "דירה" assert filters.min_rooms == 2.0 @@ -425,45 +371,42 @@ class TestDealFilters: class TestModelIntegration: """Integration tests for models working together.""" - def test_deal_to_statistics_workflow(self): - """Test creating deals and calculating statistics.""" - # Import the function to test integration - from nadlan_mcp.govmap.statistics import calculate_deal_statistics + def test_deal_to_statistics_workflow(self): + """Test creating deals and calculating statistics.""" + # Import the function to test integration + from nadlan_mcp.govmap.statistics import calculate_deal_statistics - deals = [ - Deal( - objectid=1, - deal_amount=1000000.0, - deal_date="2024-01-01", - asset_area=100.0, - property_type_description="דירה" - ), - Deal( - objectid=2, - deal_amount=2000000.0, - deal_date="2024-01-02", - asset_area=100.0, - property_type_description="דירה" - ), - ] + deals = [ + Deal( + objectid=1, + deal_amount=1000000.0, + deal_date="2024-01-01", + asset_area=100.0, + property_type_description="דירה", + ), + Deal( + objectid=2, + deal_amount=2000000.0, + deal_date="2024-01-02", + asset_area=100.0, + property_type_description="דירה", + ), + ] - stats = calculate_deal_statistics(deals) + stats = calculate_deal_statistics(deals) - assert isinstance(stats, DealStatistics) - assert stats.total_deals == 2 - assert stats.price_statistics["mean"] == 1500000.0 - assert stats.price_per_sqm_statistics["mean"] == 15000.0 - assert stats.property_type_distribution["דירה"] == 2 + assert isinstance(stats, DealStatistics) + assert stats.total_deals == 2 + assert stats.price_statistics["mean"] == 1500000.0 + assert stats.price_per_sqm_statistics["mean"] == 15000.0 + assert stats.property_type_distribution["דירה"] == 2 def test_autocomplete_to_deals_workflow(self): """Test autocomplete response leading to deal search.""" # Simulate autocomplete response coord = CoordinatePoint(longitude=180000.0, latitude=650000.0) result = AutocompleteResult( - text="סוקולוב 38 חולון", - id="addr123", - type="address", - coordinates=coord + text="סוקולוב 38 חולון", id="addr123", type="address", coordinates=coord ) response = AutocompleteResponse(resultsCount=1, results=[result]) @@ -478,14 +421,18 @@ class TestModelIntegration: min_rooms=3.0, max_rooms=4.0, min_price=1000000.0, - max_price=2000000.0 + max_price=2000000.0, ) # Create test deals deals = [ Deal(objectid=1, deal_amount=1200000.0, deal_date="2024-01-01", rooms=3.0), - Deal(objectid=2, deal_amount=2500000.0, deal_date="2024-01-02", rooms=4.0), # Price too high - Deal(objectid=3, deal_amount=1500000.0, deal_date="2024-01-03", rooms=2.0), # Too few rooms + Deal( + objectid=2, deal_amount=2500000.0, deal_date="2024-01-02", rooms=4.0 + ), # Price too high + Deal( + objectid=3, deal_amount=1500000.0, deal_date="2024-01-03", rooms=2.0 + ), # Too few rooms ] # Manually check which deals would pass diff --git a/tests/govmap/test_statistics.py b/tests/govmap/test_statistics.py index 3444910..fa14113 100644 --- a/tests/govmap/test_statistics.py +++ b/tests/govmap/test_statistics.py @@ -4,10 +4,12 @@ Tests for nadlan_mcp.govmap.statistics module. Comprehensive tests for statistical calculation functions. """ -import pytest from datetime import date -from nadlan_mcp.govmap.statistics import calculate_deal_statistics, calculate_std_dev + +import pytest + from nadlan_mcp.govmap.models import Deal, DealStatistics +from nadlan_mcp.govmap.statistics import calculate_deal_statistics, calculate_std_dev class TestCalculateDealStatistics: @@ -135,8 +137,12 @@ class TestCalculateDealStatistics: """Test statistics when some deals have zero prices.""" deals = [ Deal(objectid=1, deal_amount=1000000.0, deal_date="2024-01-01", asset_area=80.0), - Deal(objectid=2, deal_amount=0.0, deal_date="2024-01-02", asset_area=100.0), # Zero price excluded - Deal(objectid=3, deal_amount=-1.0, deal_date="2024-01-03", asset_area=120.0), # Negative excluded + Deal( + objectid=2, deal_amount=0.0, deal_date="2024-01-02", asset_area=100.0 + ), # Zero price excluded + Deal( + objectid=3, deal_amount=-1.0, deal_date="2024-01-03", asset_area=120.0 + ), # Negative excluded ] stats = calculate_deal_statistics(deals) @@ -161,8 +167,18 @@ class TestCalculateDealStatistics: def test_deals_with_missing_property_types(self): """Test statistics when some deals have missing property types.""" deals = [ - Deal(objectid=1, deal_amount=1000000.0, deal_date="2024-01-01", property_type_description="דירה"), - Deal(objectid=2, deal_amount=1500000.0, deal_date="2024-01-02", property_type_description=None), + Deal( + objectid=1, + deal_amount=1000000.0, + deal_date="2024-01-01", + property_type_description="דירה", + ), + Deal( + objectid=2, + deal_amount=1500000.0, + deal_date="2024-01-02", + property_type_description=None, + ), Deal(objectid=3, deal_amount=2000000.0, deal_date="2024-01-03"), # No property_type ] stats = calculate_deal_statistics(deals) @@ -196,7 +212,13 @@ class TestCalculateDealStatistics: def test_single_deal(self): """Test statistics with single deal.""" deals = [ - Deal(objectid=1, deal_amount=1000000.0, deal_date="2024-01-01", asset_area=80.0, property_type_description="דירה") + Deal( + objectid=1, + deal_amount=1000000.0, + deal_date="2024-01-01", + asset_area=80.0, + property_type_description="דירה", + ) ] stats = calculate_deal_statistics(deals) @@ -238,8 +260,18 @@ class TestCalculateDealStatistics: def test_date_handling_with_iso_strings(self): """Test date range calculation with ISO format strings.""" deals = [ - Deal(objectid=1, deal_amount=1000000.0, deal_date="2024-01-15T00:00:00.000Z", asset_area=80.0), - Deal(objectid=2, deal_amount=1500000.0, deal_date="2024-02-01T12:30:45.123Z", asset_area=100.0), + Deal( + objectid=1, + deal_amount=1000000.0, + deal_date="2024-01-15T00:00:00.000Z", + asset_area=80.0, + ), + Deal( + objectid=2, + deal_amount=1500000.0, + deal_date="2024-02-01T12:30:45.123Z", + asset_area=100.0, + ), ] stats = calculate_deal_statistics(deals) @@ -250,9 +282,24 @@ class TestCalculateDealStatistics: def test_multiple_same_property_types(self): """Test property type distribution with duplicates.""" deals = [ - Deal(objectid=1, deal_amount=1000000.0, deal_date="2024-01-01", property_type_description="דירה"), - Deal(objectid=2, deal_amount=1500000.0, deal_date="2024-01-02", property_type_description="דירה"), - Deal(objectid=3, deal_amount=2000000.0, deal_date="2024-01-03", property_type_description="דירה"), + Deal( + objectid=1, + deal_amount=1000000.0, + deal_date="2024-01-01", + property_type_description="דירה", + ), + Deal( + objectid=2, + deal_amount=1500000.0, + deal_date="2024-01-02", + property_type_description="דירה", + ), + Deal( + objectid=3, + deal_amount=2000000.0, + deal_date="2024-01-03", + property_type_description="דירה", + ), ] stats = calculate_deal_statistics(deals) @@ -289,7 +336,7 @@ class TestCalculateDealStatistics: Deal( objectid=i, deal_amount=1000000.0 if has_price else 0.0, # Use 0.0 instead of None - deal_date=f"2024-01-{i+1:02d}", + deal_date=f"2024-01-{i + 1:02d}", asset_area=80.0 if has_area else None, ) ) diff --git a/tests/govmap/test_utils.py b/tests/govmap/test_utils.py index 8af6460..ace5376 100644 --- a/tests/govmap/test_utils.py +++ b/tests/govmap/test_utils.py @@ -4,11 +4,10 @@ Unit tests for utils module. Tests helper utilities including distance calculation, address matching, and floor parsing. """ -import pytest from nadlan_mcp.govmap.utils import ( calculate_distance, - is_same_building, extract_floor_number, + is_same_building, ) diff --git a/tests/govmap/test_validators.py b/tests/govmap/test_validators.py index 55be4dc..45eeae7 100644 --- a/tests/govmap/test_validators.py +++ b/tests/govmap/test_validators.py @@ -5,11 +5,12 @@ Tests input validation functions for addresses, coordinates, integers, and deal """ import pytest + from nadlan_mcp.govmap.validators import ( validate_address, validate_coordinates, - validate_positive_int, validate_deal_type, + validate_positive_int, ) diff --git a/tests/test_fastmcp_tools.py b/tests/test_fastmcp_tools.py index 7207d3f..6064b46 100644 --- a/tests/test_fastmcp_tools.py +++ b/tests/test_fastmcp_tools.py @@ -8,19 +8,22 @@ Updated for Phase 4.1 - Pydantic models integration. """ import json -import pytest -from unittest.mock import Mock, patch +from unittest.mock import patch + from nadlan_mcp import fastmcp_server from nadlan_mcp.govmap.models import ( - Deal, AutocompleteResponse, AutocompleteResult, CoordinatePoint, - DealStatistics, MarketActivityScore, InvestmentAnalysis, LiquidityMetrics + AutocompleteResponse, + AutocompleteResult, + CoordinatePoint, + Deal, + DealStatistics, ) class TestAutocompleteAddress: """Test autocomplete_address MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_autocomplete(self, mock_client): """Test successful address autocomplete with correct field mapping.""" # Now returns AutocompleteResponse model @@ -33,7 +36,7 @@ class TestAutocompleteAddress: type="address", score=100, coordinates=CoordinatePoint(longitude=180000.5, latitude=650000.3), - shape="POINT(180000.5 650000.3)" + shape="POINT(180000.5 650000.3)", ), AutocompleteResult( id="address|ADDR|124", @@ -41,9 +44,9 @@ class TestAutocompleteAddress: type="address", score=95, coordinates=CoordinatePoint(longitude=180010.2, latitude=650005.7), - shape="POINT(180010.2 650005.7)" - ) - ] + shape="POINT(180010.2 650005.7)", + ), + ], ) result = fastmcp_server.autocomplete_address("דיזנגוף תל אביב") @@ -56,20 +59,19 @@ class TestAutocompleteAddress: assert parsed[0]["coordinates"]["longitude"] == 180000.5 assert parsed[0]["coordinates"]["latitude"] == 650000.3 - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_autocomplete_no_results(self, mock_client): """Test autocomplete with no results.""" # Now returns AutocompleteResponse model mock_client.autocomplete_address.return_value = AutocompleteResponse( - resultsCount=0, - results=[] + resultsCount=0, results=[] ) result = fastmcp_server.autocomplete_address("nonexistent address") # With empty results, returns a message string assert "No addresses found" in result - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_autocomplete_invalid_coordinates(self, mock_client): """Test autocomplete with invalid/missing coordinate format.""" # Now returns AutocompleteResponse model with result that has no coordinates @@ -82,9 +84,9 @@ class TestAutocompleteAddress: type="address", score=100, coordinates=None, # No coordinates parsed - shape="INVALID_FORMAT" + shape="INVALID_FORMAT", ) - ] + ], ) result = fastmcp_server.autocomplete_address("test") @@ -93,7 +95,7 @@ class TestAutocompleteAddress: assert len(parsed) == 1 assert parsed[0]["text"] == "דיזנגוף 50" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_autocomplete_missing_shape(self, mock_client): """Test autocomplete with missing shape field.""" # Mock with AutocompleteResponse model @@ -105,9 +107,9 @@ class TestAutocompleteAddress: text="דיזנגוף 50", type="address", score=100, - coordinates=None # No coordinates + coordinates=None, # No coordinates ) - ] + ], ) result = fastmcp_server.autocomplete_address("test") @@ -115,7 +117,7 @@ class TestAutocompleteAddress: # When coordinates are None, the field isn't included in the response assert "coordinates" not in parsed[0] - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_autocomplete_error_handling(self, mock_client): """Test autocomplete error handling.""" mock_client.autocomplete_address.side_effect = Exception("API Error") @@ -128,7 +130,7 @@ class TestAutocompleteAddress: class TestGetDealsByRadius: """Test get_deals_by_radius MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_get_deals(self, mock_client): """Test successful polygon metadata retrieval.""" # Mock with polygon metadata dicts (not Deal objects) @@ -139,7 +141,7 @@ class TestGetDealsByRadius: "settlementNameHeb": "תל אביב-יפו", "streetNameHeb": "דיזנגוף", "houseNum": 50, - "polygon_id": "123-456" + "polygon_id": "123-456", } ] mock_client.get_deals_by_radius.return_value = mock_polygons @@ -152,7 +154,7 @@ class TestGetDealsByRadius: assert parsed["total_polygons"] == 1 mock_client.get_deals_by_radius.assert_called_once() - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_get_deals_no_results(self, mock_client): """Test polygon metadata retrieval with no results.""" mock_client.get_deals_by_radius.return_value = [] @@ -160,7 +162,7 @@ class TestGetDealsByRadius: result = fastmcp_server.get_deals_by_radius(650000.0, 180000.0, 500) assert "No polygons found" in result - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_get_deals_strips_bloat_fields(self, mock_client): """Test that polygon metadata is returned as-is.""" # Mock with polygon metadata dicts @@ -169,7 +171,7 @@ class TestGetDealsByRadius: "objectid": 123, "dealscount": "10", "polygon_id": "abc123", - "settlementNameHeb": "Tel Aviv" + "settlementNameHeb": "Tel Aviv", } ] mock_client.get_deals_by_radius.return_value = mock_polygons @@ -182,7 +184,7 @@ class TestGetDealsByRadius: assert polygon["polygon_id"] == "abc123" assert polygon["dealscount"] == "10" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_get_deals_error_handling(self, mock_client): """Test error handling for deal retrieval.""" mock_client.get_deals_by_radius.side_effect = ValueError("Invalid coordinates") @@ -194,7 +196,7 @@ class TestGetDealsByRadius: class TestFindRecentDealsForAddress: """Test find_recent_deals_for_address MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_find_deals(self, mock_client): """Test successful deal finding with statistics.""" # Mock with Deal models @@ -204,21 +206,19 @@ class TestFindRecentDealsForAddress: deal_amount=2000000, deal_date="2023-01-01", asset_area=80.0, - priority=0 + priority=0, ), Deal( objectid=124, deal_amount=1800000, deal_date="2023-01-02", asset_area=70.0, - priority=1 - ) + priority=1, + ), ] mock_client.find_recent_deals_for_address.return_value = mock_deals - result = fastmcp_server.find_recent_deals_for_address( - "דיזנגוף 50 תל אביב", 2, 100, 100 - ) + result = fastmcp_server.find_recent_deals_for_address("דיזנגוף 50 תל אביב", 2, 100, 100) parsed = json.loads(result) assert "search_parameters" in parsed @@ -227,20 +227,18 @@ class TestFindRecentDealsForAddress: assert len(parsed["deals"]) == 2 assert parsed["market_statistics"]["deal_breakdown"]["total_deals"] == 2 - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_find_deals_no_results(self, mock_client): """Test deal finding with no results.""" mock_client.find_recent_deals_for_address.return_value = [] - result = fastmcp_server.find_recent_deals_for_address( - "nonexistent address", 2, 100, 100 - ) + result = fastmcp_server.find_recent_deals_for_address("nonexistent address", 2, 100, 100) # When no deals, returns a text message, not JSON assert "No second hand (used) deals found" in result assert "nonexistent address" in result - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_find_deals_strips_bloat(self, mock_client): """Test that bloat fields are stripped.""" # Mock with Deal models @@ -250,14 +248,12 @@ class TestFindRecentDealsForAddress: deal_amount=2000000, deal_date="2023-01-01", shape="MULTIPOLYGON(...)", - sourceorder=1 + sourceorder=1, ) ] mock_client.find_recent_deals_for_address.return_value = mock_deals - result = fastmcp_server.find_recent_deals_for_address( - "test address", 2, 100, 100 - ) + result = fastmcp_server.find_recent_deals_for_address("test address", 2, 100, 100) parsed = json.loads(result) assert "shape" not in parsed["deals"][0] @@ -267,7 +263,7 @@ class TestFindRecentDealsForAddress: class TestAnalyzeMarketTrends: """Test analyze_market_trends MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_market_analysis(self, mock_client): """Test successful market trend analysis.""" # Mock with Deal models @@ -279,7 +275,7 @@ class TestAnalyzeMarketTrends: asset_area=80.0, property_type_description="דירה", neighborhood="תל אביב", - priority=1 + priority=1, ) ] mock_client.find_recent_deals_for_address.return_value = mock_deals @@ -292,7 +288,7 @@ class TestAnalyzeMarketTrends: assert "yearly_trends" in parsed assert "top_property_types" in parsed - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_market_analysis_no_data(self, mock_client): """Test market analysis with no data.""" mock_client.find_recent_deals_for_address.return_value = [] @@ -307,25 +303,23 @@ class TestAnalyzeMarketTrends: class TestCompareAddresses: """Test compare_addresses MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_comparison(self, mock_client): """Test successful address comparison.""" # Mock different deals for each address mock_client.find_recent_deals_for_address.side_effect = [ [{"dealAmount": 2000000, "assetArea": 80, "price_per_sqm": 25000}], - [{"dealAmount": 1500000, "assetArea": 60, "price_per_sqm": 25000}] + [{"dealAmount": 1500000, "assetArea": 60, "price_per_sqm": 25000}], ] - result = fastmcp_server.compare_addresses( - ["דיזנגוף 50 תל אביב", "הרצל 1 חולון"] - ) + result = fastmcp_server.compare_addresses(["דיזנגוף 50 תל אביב", "הרצל 1 חולון"]) parsed = json.loads(result) assert "addresses_compared" in parsed assert parsed["addresses_compared"] == 2 assert "all_results" in parsed - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_comparison_error_handling(self, mock_client): """Test error handling in address comparison.""" mock_client.find_recent_deals_for_address.side_effect = ValueError("Invalid address") @@ -340,10 +334,10 @@ class TestCompareAddresses: class TestGetValuationComparables: """Test get_valuation_comparables MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_get_comparables(self, mock_client): """Test successful comparable retrieval with filtering.""" - from nadlan_mcp.govmap.models import DealStatistics + # Mock with Deal models mock_deals = [ Deal( @@ -352,24 +346,21 @@ class TestGetValuationComparables: deal_date="2023-01-01", asset_area=80.0, rooms=3.0, - property_type_description="דירה" + property_type_description="דירה", ) ] mock_stats = DealStatistics( total_deals=1, price_statistics={"mean": 2000000}, area_statistics={"mean": 80}, - price_per_sqm_statistics={"mean": 25000} + price_per_sqm_statistics={"mean": 25000}, ) mock_client.find_recent_deals_for_address.return_value = mock_deals mock_client.filter_deals_by_criteria.return_value = mock_deals mock_client.calculate_deal_statistics.return_value = mock_stats result = fastmcp_server.get_valuation_comparables( - "דיזנגוף 50 תל אביב", - property_type="דירה", - min_rooms=2, - max_rooms=4 + "דיזנגוף 50 תל אביב", property_type="דירה", min_rooms=2, max_rooms=4 ) parsed = json.loads(result) @@ -378,10 +369,10 @@ class TestGetValuationComparables: assert "comparables" in parsed assert parsed["filters_applied"]["property_type"] == "דירה" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_comparables_strips_bloat(self, mock_client): """Test that bloat fields are stripped from comparables.""" - from nadlan_mcp.govmap.models import DealStatistics + # Mock with Deal models mock_deals = [ Deal( @@ -390,13 +381,10 @@ class TestGetValuationComparables: deal_date="2023-01-01", shape="MULTIPOLYGON(...)", sourceorder=1, - source_polygon_id="abc" + source_polygon_id="abc", ) ] - mock_stats = DealStatistics( - total_deals=1, - price_statistics={"mean": 2000000} - ) + mock_stats = DealStatistics(total_deals=1, price_statistics={"mean": 2000000}) mock_client.find_recent_deals_for_address.return_value = mock_deals mock_client.filter_deals_by_criteria.return_value = mock_deals mock_client.calculate_deal_statistics.return_value = mock_stats @@ -413,23 +401,18 @@ class TestGetValuationComparables: class TestGetDealStatistics: """Test get_deal_statistics MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_statistics_calculation(self, mock_client): """Test successful statistics calculation.""" - from nadlan_mcp.govmap.models import DealStatistics + # Mock with Deal models mock_deals = [ Deal(objectid=1, deal_amount=2000000, deal_date="2023-01-01", asset_area=80.0), - Deal(objectid=2, deal_amount=1800000, deal_date="2023-01-02", asset_area=70.0) + Deal(objectid=2, deal_amount=1800000, deal_date="2023-01-02", asset_area=70.0), ] mock_stats = DealStatistics( total_deals=2, - price_statistics={ - "mean": 1900000, - "median": 1900000, - "min": 1800000, - "max": 2000000 - } + price_statistics={"mean": 1900000, "median": 1900000, "min": 1800000, "max": 2000000}, ) mock_client.find_recent_deals_for_address.return_value = mock_deals mock_client.filter_deals_by_criteria.return_value = mock_deals @@ -446,28 +429,24 @@ class TestGetDealStatistics: class TestGetMarketActivityMetrics: """Test get_market_activity_metrics MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_activity_metrics(self, mock_client): """Test successful market activity calculation.""" mock_deals = [ - { - "dealDate": "2024-01-15T00:00:00.000Z", - "dealAmount": 2000000, - "assetArea": 80 - } + {"dealDate": "2024-01-15T00:00:00.000Z", "dealAmount": 2000000, "assetArea": 80} ] mock_client.find_recent_deals_for_address.return_value = mock_deals mock_client.calculate_market_activity_score.return_value = { "activity_score": 75, - "activity_level": "high" + "activity_level": "high", } mock_client.get_market_liquidity.return_value = { "velocity_score": 8.5, - "liquidity_rating": "high" + "liquidity_rating": "high", } mock_client.analyze_investment_potential.return_value = { "investment_score": 80, - "recommendation": "positive" + "recommendation": "positive", } result = fastmcp_server.get_market_activity_metrics("test address") @@ -481,13 +460,11 @@ class TestGetMarketActivityMetrics: class TestGetStreetDeals: """Test get_street_deals MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_street_deals(self, mock_client): """Test successful street deal retrieval.""" # Mock with Deal models - mock_deals = [ - Deal(objectid=123, deal_amount=2000000, deal_date="2023-01-01") - ] + mock_deals = [Deal(objectid=123, deal_amount=2000000, deal_date="2023-01-01")] mock_client.get_street_deals.return_value = mock_deals result = fastmcp_server.get_street_deals("12345", 100) @@ -500,13 +477,11 @@ class TestGetStreetDeals: class TestGetNeighborhoodDeals: """Test get_neighborhood_deals MCP tool.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_successful_neighborhood_deals(self, mock_client): """Test successful neighborhood deal retrieval.""" # Mock with Deal models - mock_deals = [ - Deal(objectid=123, deal_amount=2000000, deal_date="2023-01-01") - ] + mock_deals = [Deal(objectid=123, deal_amount=2000000, deal_date="2023-01-01")] mock_client.get_neighborhood_deals.return_value = mock_deals result = fastmcp_server.get_neighborhood_deals("12345", 100) diff --git a/tests/test_govmap_client.py b/tests/test_govmap_client.py index 1df5b4b..dd30b8c 100644 --- a/tests/test_govmap_client.py +++ b/tests/test_govmap_client.py @@ -4,33 +4,34 @@ Tests for the GovmapClient class. Updated for Phase 4.1 - Pydantic models integration. """ -import pytest -import requests from unittest.mock import Mock, patch -from nadlan_mcp.govmap import GovmapClient -from nadlan_mcp.govmap.models import Deal, AutocompleteResponse, AutocompleteResult, CoordinatePoint + +import pytest + from nadlan_mcp.config import GovmapConfig +from nadlan_mcp.govmap import GovmapClient +from nadlan_mcp.govmap.models import AutocompleteResponse, AutocompleteResult, CoordinatePoint, Deal class TestGovmapClient: """Test cases for GovmapClient class.""" - + def test_client_initialization(self): """Test that GovmapClient initializes correctly.""" client = GovmapClient() assert client.base_url == "https://www.govmap.gov.il/api" assert client.session is not None - assert client.session.headers['Content-Type'] == 'application/json' - assert client.session.headers['User-Agent'] == 'NadlanMCP/1.0.0' - + assert client.session.headers["Content-Type"] == "application/json" + assert client.session.headers["User-Agent"] == "NadlanMCP/1.0.0" + def test_client_initialization_with_custom_url(self): """Test that GovmapClient can be initialized with custom URL.""" custom_url = "https://custom-api.example.com/api/" custom_config = GovmapConfig(base_url=custom_url) client = GovmapClient(custom_config) assert client.base_url == "https://custom-api.example.com/api" - - @patch('requests.Session') + + @patch("requests.Session") def test_autocomplete_address_success(self, mock_session_class): """Test successful address autocomplete.""" # Mock response @@ -44,9 +45,9 @@ class TestGovmapClient: "type": "address", "score": 100, "shape": "POINT(3870000.123 3770000.456)", - "data": {} + "data": {}, } - ] + ], } mock_response.raise_for_status.return_value = None @@ -65,8 +66,8 @@ class TestGovmapClient: assert result.results[0].coordinates is not None assert result.results[0].coordinates.longitude == 3870000.123 mock_session.post.assert_called_once() - - @patch('requests.Session') + + @patch("requests.Session") def test_autocomplete_address_empty_results(self, mock_session_class): """Test autocomplete with empty results - should return empty results, not raise error.""" mock_response = Mock() @@ -83,23 +84,23 @@ class TestGovmapClient: assert isinstance(result, AutocompleteResponse) assert result.results_count == 0 assert len(result.results) == 0 - - @patch('requests.Session') + + @patch("requests.Session") def test_autocomplete_address_invalid_response(self, mock_session_class): """Test autocomplete with truly invalid response format.""" mock_response = Mock() mock_response.json.return_value = {"invalid": "response"} # Missing 'results' key mock_response.raise_for_status.return_value = None - + mock_session = Mock() mock_session.post.return_value = mock_response mock_session_class.return_value = mock_session - + client = GovmapClient() - + with pytest.raises(ValueError, match="Invalid response format"): client.autocomplete_address("test") - + def test_coordinate_parsing_from_wkt_point(self): """Test coordinate parsing from WKT POINT format.""" client = GovmapClient() @@ -113,20 +114,20 @@ class TestGovmapClient: text="test address", type="address", shape="POINT(3870000.123 3770000.456)", - coordinates=CoordinatePoint(longitude=3870000.123, latitude=3770000.456) + coordinates=CoordinatePoint(longitude=3870000.123, latitude=3770000.456), ) - ] + ], ) # We'll test the coordinate parsing logic by calling the method that uses it - with patch.object(client, 'autocomplete_address', return_value=mock_autocomplete_result): - with patch.object(client, 'get_deals_by_radius', return_value=[]): - with patch.object(client, 'get_street_deals', return_value=[]): - with patch.object(client, 'get_neighborhood_deals', return_value=[]): + with patch.object(client, "autocomplete_address", return_value=mock_autocomplete_result): + with patch.object(client, "get_deals_by_radius", return_value=[]): + with patch.object(client, "get_street_deals", return_value=[]): + with patch.object(client, "get_neighborhood_deals", return_value=[]): result = client.find_recent_deals_for_address("test", years_back=1) assert result == [] - - @patch('requests.Session') + + @patch("requests.Session") def test_get_deals_by_radius_success(self, mock_session_class): """Test successful polygon metadata retrieval by radius.""" mock_response = Mock() @@ -138,7 +139,7 @@ class TestGovmapClient: "settlementNameHeb": "תל אביב-יפו", "streetNameHeb": "דיזנגוף", "houseNum": 50, - "polygon_id": "123-456" + "polygon_id": "123-456", } ] mock_response.raise_for_status.return_value = None @@ -156,8 +157,8 @@ class TestGovmapClient: assert result[0]["objectid"] == 12345 assert result[0]["polygon_id"] == "123-456" mock_session.get.assert_called_once() - - @patch('requests.Session') + + @patch("requests.Session") def test_get_street_deals_success(self, mock_session_class): """Test successful street deals query.""" mock_response = Mock() @@ -170,16 +171,16 @@ class TestGovmapClient: "dealDate": "2025-01-01T00:00:00.000Z", "assetArea": 100, "settlementNameHeb": "תל אביב-יפו", - "propertyTypeDescription": "דירה" + "propertyTypeDescription": "דירה", } - ] + ], } mock_response.raise_for_status.return_value = None - + mock_session = Mock() mock_session.get.return_value = mock_response mock_session_class.return_value = mock_session - + client = GovmapClient() result = client.get_street_deals("123-456") @@ -190,8 +191,8 @@ class TestGovmapClient: assert result[0].asset_area == 100 assert result[0].price_per_sqm == 10000.0 # Computed field mock_session.get.assert_called_once() - - @patch('requests.Session') + + @patch("requests.Session") def test_get_neighborhood_deals_success(self, mock_session_class): """Test successful neighborhood deals query.""" mock_response = Mock() @@ -204,16 +205,16 @@ class TestGovmapClient: "dealDate": "2025-01-15T00:00:00.000Z", "assetArea": 120, "settlementNameHeb": "תל אביב-יפו", - "propertyTypeDescription": "דירה" + "propertyTypeDescription": "דירה", } - ] + ], } mock_response.raise_for_status.return_value = None - + mock_session = Mock() mock_session.get.return_value = mock_response mock_session_class.return_value = mock_session - + client = GovmapClient() result = client.get_neighborhood_deals("123-456") @@ -224,14 +225,20 @@ class TestGovmapClient: assert result[0].asset_area == 120 assert result[0].price_per_sqm == round(2000000 / 120, 2) mock_session.get.assert_called_once() - - @patch('nadlan_mcp.govmap.client.GovmapClient.get_neighborhood_deals') - @patch('nadlan_mcp.govmap.client.GovmapClient.get_street_deals') - @patch('nadlan_mcp.govmap.client.GovmapClient.get_deals_by_radius') - @patch('nadlan_mcp.govmap.client.GovmapClient.autocomplete_address') - def test_find_recent_deals_for_address_integration(self, mock_autocomplete, mock_radius, mock_street, mock_neighborhood): + + @patch("nadlan_mcp.govmap.client.GovmapClient.get_neighborhood_deals") + @patch("nadlan_mcp.govmap.client.GovmapClient.get_street_deals") + @patch("nadlan_mcp.govmap.client.GovmapClient.get_deals_by_radius") + @patch("nadlan_mcp.govmap.client.GovmapClient.autocomplete_address") + def test_find_recent_deals_for_address_integration( + self, mock_autocomplete, mock_radius, mock_street, mock_neighborhood + ): """Test the main integration function.""" - from nadlan_mcp.govmap.models import CoordinatePoint, AutocompleteResult, AutocompleteResponse + from nadlan_mcp.govmap.models import ( + AutocompleteResponse, + AutocompleteResult, + CoordinatePoint, + ) # Mock autocomplete response - now returns AutocompleteResponse model mock_autocomplete.return_value = AutocompleteResponse( @@ -242,14 +249,19 @@ class TestGovmapClient: id="addr123", type="address", coordinates=CoordinatePoint(longitude=3870000.123, latitude=3770000.456), - shape="POINT(3870000.123 3770000.456)" + shape="POINT(3870000.123 3770000.456)", ) - ] + ], ) # Mock radius response - now returns List[Dict] (polygon metadata) mock_radius.return_value = [ - {"objectid": 1, "dealscount": "10", "polygon_id": "123-456", "settlementNameHeb": "Tel Aviv"} + { + "objectid": 1, + "dealscount": "10", + "polygon_id": "123-456", + "settlementNameHeb": "Tel Aviv", + } ] # Mock street deals response - now returns List[Deal] @@ -259,7 +271,7 @@ class TestGovmapClient: deal_amount=1000000, deal_date="2025-01-01T00:00:00.000Z", street_name="Test Street", - house_number="1" + house_number="1", ) ] @@ -270,7 +282,7 @@ class TestGovmapClient: deal_amount=2000000, deal_date="2025-01-15T00:00:00.000Z", street_name="Test Street", - house_number="2" + house_number="2", ) ] @@ -284,25 +296,25 @@ class TestGovmapClient: # Should be sorted by priority first (street=1 before neighborhood=2), then by date # Priority is set dynamically by find_recent_deals_for_address - assert hasattr(result[0], 'priority') - assert hasattr(result[1], 'priority') + assert hasattr(result[0], "priority") + assert hasattr(result[1], "priority") assert result[0].priority <= result[1].priority # Lower priority comes first - - @patch('requests.Session') + + @patch("requests.Session") def test_http_error_handling(self, mock_session_class): """Test that HTTP errors are properly handled.""" mock_response = Mock() mock_response.raise_for_status.side_effect = Exception("HTTP Error") - + mock_session = Mock() mock_session.post.return_value = mock_response mock_session_class.return_value = mock_session - + client = GovmapClient() - + with pytest.raises(Exception, match="HTTP Error"): client.autocomplete_address("test") - + def test_invalid_coordinate_format(self): """Test handling of invalid coordinate formats.""" client = GovmapClient() @@ -316,12 +328,12 @@ class TestGovmapClient: text="test address", type="address", shape="INVALID_FORMAT", # Invalid format - coordinates=None # No coordinates + coordinates=None, # No coordinates ) - ] + ], ) - with patch.object(client, 'autocomplete_address', return_value=mock_autocomplete_result): + with patch.object(client, "autocomplete_address", return_value=mock_autocomplete_result): with pytest.raises(ValueError, match="No coordinates found"): client.find_recent_deals_for_address("test", years_back=1) @@ -332,18 +344,21 @@ class TestMarketAnalysisFunctions: def test_calculate_market_activity_score_success(self): """Test successful market activity score calculation.""" from nadlan_mcp.govmap.models import MarketActivityScore + client = GovmapClient() # Sample deals with dates - now using Deal models deals = [ Deal(objectid=i, deal_date=date, deal_amount=amount) - for i, (date, amount) in enumerate([ - ("2023-01-15", 1000000), - ("2023-01-20", 1100000), - ("2023-02-10", 1200000), - ("2023-03-05", 1150000), - ("2023-04-12", 1250000), - ]) + for i, (date, amount) in enumerate( + [ + ("2023-01-15", 1000000), + ("2023-01-20", 1100000), + ("2023-02-10", 1200000), + ("2023-03-05", 1150000), + ("2023-04-12", 1250000), + ] + ) ] result = client.calculate_market_activity_score(deals, time_period_months=None) @@ -360,14 +375,18 @@ class TestMarketAnalysisFunctions: """Test market activity score with empty deals list.""" client = GovmapClient() - with pytest.raises(ValueError, match="Cannot calculate market activity from empty deals list"): + with pytest.raises( + ValueError, match="Cannot calculate market activity from empty deals list" + ): client.calculate_market_activity_score([]) def test_calculate_market_activity_score_with_time_filter(self): """Test market activity score with time period filtering.""" # Note: With Pydantic models, deal_date is required and validated from datetime import datetime, timedelta + from nadlan_mcp.govmap.models import MarketActivityScore + client = GovmapClient() # Create deals spanning several months using recent dates @@ -376,7 +395,7 @@ class TestMarketAnalysisFunctions: Deal( objectid=i, deal_date=(today - timedelta(days=30 * month)).strftime("%Y-%m-%d"), - deal_amount=1000000 + i * 10000 + deal_amount=1000000 + i * 10000, ) for i, month in enumerate([1, 1, 2, 3, 3, 3, 6, 11], 1) # All within last 12 months ] @@ -392,7 +411,9 @@ class TestMarketAnalysisFunctions: # Generate many deals across multiple months for trend analysis - now using Deal models deals = [ - Deal(objectid=i, deal_date=f"2023-{(i % 6) + 1:02d}-15", deal_amount=1000000 + i * 10000) + Deal( + objectid=i, deal_date=f"2023-{(i % 6) + 1:02d}-15", deal_amount=1000000 + i * 10000 + ) for i in range(1, 31) # 30 deals spread across 6 months ] @@ -405,31 +426,34 @@ class TestMarketAnalysisFunctions: def test_analyze_investment_potential_success(self): """Test successful investment potential analysis.""" from nadlan_mcp.govmap.models import InvestmentAnalysis + client = GovmapClient() # Sample deals with price appreciation - now using Deal models # Note: price_per_sqm is computed automatically from deal_amount / asset_area deals = [ Deal(objectid=i, deal_date=date, deal_amount=amount, asset_area=80.0) - for i, (date, amount) in enumerate([ - ("2022-01-15", 1000000), - ("2022-06-10", 1050000), - ("2023-01-05", 1100000), - ("2023-06-12", 1150000), - ]) + for i, (date, amount) in enumerate( + [ + ("2022-01-15", 1000000), + ("2022-06-10", 1050000), + ("2023-01-05", 1100000), + ("2023-06-12", 1150000), + ] + ) ] result = client.analyze_investment_potential(deals) # Now returns InvestmentAnalysis model assert isinstance(result, InvestmentAnalysis) - assert hasattr(result, 'price_appreciation_rate') - assert hasattr(result, 'price_volatility') - assert hasattr(result, 'market_stability') - assert hasattr(result, 'price_trend') - assert hasattr(result, 'avg_price_per_sqm') - assert hasattr(result, 'investment_score') - assert hasattr(result, 'data_quality') + assert hasattr(result, "price_appreciation_rate") + assert hasattr(result, "price_volatility") + assert hasattr(result, "market_stability") + assert hasattr(result, "price_trend") + assert hasattr(result, "avg_price_per_sqm") + assert hasattr(result, "investment_score") + assert hasattr(result, "data_quality") assert 0 <= result.investment_score <= 100 assert result.price_trend in ["increasing", "stable", "decreasing"] @@ -437,7 +461,9 @@ class TestMarketAnalysisFunctions: """Test investment potential with empty deals list.""" client = GovmapClient() - with pytest.raises(ValueError, match="Cannot analyze investment potential from empty deals list"): + with pytest.raises( + ValueError, match="Cannot analyze investment potential from empty deals list" + ): client.analyze_investment_potential([]) def test_analyze_investment_potential_insufficient_data(self): @@ -459,7 +485,12 @@ class TestMarketAnalysisFunctions: # Deals with consistent prices (very stable) - now using Deal models deals = [ - Deal(objectid=i, deal_date=f"2023-{i:02d}-15", deal_amount=1000000 + i * 1000, asset_area=80.0) + Deal( + objectid=i, + deal_date=f"2023-{i:02d}-15", + deal_amount=1000000 + i * 1000, + asset_area=80.0, + ) for i in range(1, 13) # 12 months, slight increase ] @@ -472,19 +503,22 @@ class TestMarketAnalysisFunctions: def test_get_market_liquidity_success(self): """Test successful market liquidity calculation.""" from nadlan_mcp.govmap.models import LiquidityMetrics + client = GovmapClient() # Sample deals across multiple quarters - now using Deal models deals = [ Deal(objectid=i, deal_date=date, deal_amount=amount) - for i, (date, amount) in enumerate([ - ("2023-01-15", 1000000), - ("2023-02-20", 1100000), - ("2023-05-10", 1200000), - ("2023-06-05", 1150000), - ("2023-09-12", 1250000), - ("2023-10-18", 1300000), - ]) + for i, (date, amount) in enumerate( + [ + ("2023-01-15", 1000000), + ("2023-02-20", 1100000), + ("2023-05-10", 1200000), + ("2023-06-05", 1150000), + ("2023-09-12", 1250000), + ("2023-10-18", 1300000), + ] + ) ] result = client.get_market_liquidity(deals, time_period_months=None) @@ -500,12 +534,15 @@ class TestMarketAnalysisFunctions: """Test market liquidity with empty deals list.""" client = GovmapClient() - with pytest.raises(ValueError, match="Cannot calculate market liquidity from empty deals list"): + with pytest.raises( + ValueError, match="Cannot calculate market liquidity from empty deals list" + ): client.get_market_liquidity([]) def test_get_market_liquidity_varied_periods(self): """Test market liquidity with varied time periods.""" from datetime import datetime, timedelta + client = GovmapClient() # Deals spread across recent quarters - now using Deal models @@ -514,7 +551,7 @@ class TestMarketAnalysisFunctions: Deal( objectid=i, deal_date=(today - timedelta(days=days)).strftime("%Y-%m-%d"), - deal_amount=1000000 + deal_amount=1000000, ) for i, days in enumerate([30, 60, 150, 240, 330]) # Spread across ~11 months ] @@ -532,9 +569,27 @@ class TestMarketAnalysisFunctions: # Now using Deal models deals = [ - Deal(objectid=1, property_type_description="דירה", rooms=3, deal_amount=1000000, deal_date="2023-01-01"), - Deal(objectid=2, property_type_description="בית", rooms=5, deal_amount=2000000, deal_date="2023-01-01"), - Deal(objectid=3, property_type_description="דירה", rooms=4, deal_amount=1500000, deal_date="2023-01-01"), + Deal( + objectid=1, + property_type_description="דירה", + rooms=3, + deal_amount=1000000, + deal_date="2023-01-01", + ), + Deal( + objectid=2, + property_type_description="בית", + rooms=5, + deal_amount=2000000, + deal_date="2023-01-01", + ), + Deal( + objectid=3, + property_type_description="דירה", + rooms=4, + deal_amount=1500000, + deal_date="2023-01-01", + ), ] filtered = client.filter_deals_by_criteria(deals, property_type="דירה") @@ -551,7 +606,9 @@ class TestMarketAnalysisFunctions: # Now using Deal models deals = [ Deal(objectid=i, rooms=rooms, deal_amount=amount, deal_date="2023-01-01") - for i, (rooms, amount) in enumerate([(2, 800000), (3, 1000000), (4, 1500000), (5, 2000000)]) + for i, (rooms, amount) in enumerate( + [(2, 800000), (3, 1000000), (4, 1500000), (5, 2000000)] + ) ] filtered = client.filter_deals_by_criteria(deals, min_rooms=3, max_rooms=4) @@ -579,6 +636,7 @@ class TestMarketAnalysisFunctions: def test_calculate_deal_statistics_success(self): """Test successful deal statistics calculation.""" from nadlan_mcp.govmap.models import DealStatistics + client = GovmapClient() # Now using Deal models - price_per_sqm computed automatically @@ -620,10 +678,27 @@ class TestMarketAnalysisFunctions: """Test that deals with missing property type are excluded when filter is active.""" client = GovmapClient() deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date="2023-01-01", property_type_description="דירה"), - Deal(objectid=2, deal_amount=1000000, deal_date="2023-01-01", property_type_description=None), - Deal(objectid=3, deal_amount=1000000, deal_date="2023-01-01", property_type_description="בית"), - Deal(objectid=4, deal_amount=1000000, deal_date="2023-01-01"), # Missing property_type_description + Deal( + objectid=1, + deal_amount=1000000, + deal_date="2023-01-01", + property_type_description="דירה", + ), + Deal( + objectid=2, + deal_amount=1000000, + deal_date="2023-01-01", + property_type_description=None, + ), + Deal( + objectid=3, + deal_amount=1000000, + deal_date="2023-01-01", + property_type_description="בית", + ), + Deal( + objectid=4, deal_amount=1000000, deal_date="2023-01-01" + ), # Missing property_type_description ] filtered = client.filter_deals_by_criteria(deals, property_type="דירה") @@ -684,10 +759,18 @@ class TestMarketAnalysisFunctions: # This test now verifies filtering based on numeric ranges client = GovmapClient() deals = [ - Deal(objectid=1, deal_amount=2000000, deal_date="2023-01-01", asset_area=65.0, rooms=3.0), - Deal(objectid=2, deal_amount=2000000, deal_date="2023-01-01", asset_area=80.0, rooms=3.0), # Area too high - Deal(objectid=3, deal_amount=2000000, deal_date="2023-01-01", asset_area=65.0, rooms=5.0), # Rooms too high - Deal(objectid=4, deal_amount=3000000, deal_date="2023-01-01", asset_area=65.0, rooms=3.0), # Price too high + Deal( + objectid=1, deal_amount=2000000, deal_date="2023-01-01", asset_area=65.0, rooms=3.0 + ), + Deal( + objectid=2, deal_amount=2000000, deal_date="2023-01-01", asset_area=80.0, rooms=3.0 + ), # Area too high + Deal( + objectid=3, deal_amount=2000000, deal_date="2023-01-01", asset_area=65.0, rooms=5.0 + ), # Rooms too high + Deal( + objectid=4, deal_amount=3000000, deal_date="2023-01-01", asset_area=65.0, rooms=3.0 + ), # Price too high ] # Area filter should exclude deal 2 @@ -701,7 +784,9 @@ class TestMarketAnalysisFunctions: assert all(d.objectid in [1, 2, 4] for d in filtered_rooms) # Price filter should exclude deal 4 - filtered_price = client.filter_deals_by_criteria(deals, min_price=1500000, max_price=2500000) + filtered_price = client.filter_deals_by_criteria( + deals, min_price=1500000, max_price=2500000 + ) assert len(filtered_price) == 3 assert all(d.objectid in [1, 2, 3] for d in filtered_price) @@ -709,9 +794,26 @@ class TestMarketAnalysisFunctions: """Test that deals with missing data pass through when no filter is active for that field.""" client = GovmapClient() deals = [ - Deal(objectid=1, deal_amount=1000000, deal_date="2023-01-01", property_type_description="דירה", asset_area=65.0), - Deal(objectid=2, deal_amount=1000000, deal_date="2023-01-01", property_type_description="דירה", asset_area=None), - Deal(objectid=3, deal_amount=1000000, deal_date="2023-01-01", property_type_description="דירה"), # Missing asset_area + Deal( + objectid=1, + deal_amount=1000000, + deal_date="2023-01-01", + property_type_description="דירה", + asset_area=65.0, + ), + Deal( + objectid=2, + deal_amount=1000000, + deal_date="2023-01-01", + property_type_description="דירה", + asset_area=None, + ), + Deal( + objectid=3, + deal_amount=1000000, + deal_date="2023-01-01", + property_type_description="דירה", + ), # Missing asset_area ] # Filter by property type only - missing area should pass through diff --git a/tests/test_mcp_tools_fast.py b/tests/test_mcp_tools_fast.py index a1e5544..41884cc 100644 --- a/tests/test_mcp_tools_fast.py +++ b/tests/test_mcp_tools_fast.py @@ -6,19 +6,20 @@ without hitting the real Govmap API. For full E2E tests with real API calls, see tests/e2e/test_mcp_tools.py """ + import json -import pytest from pathlib import Path -from unittest.mock import patch, Mock -from nadlan_mcp.govmap.models import Deal, AutocompleteResult, AutocompleteResponse, CoordinatePoint +from unittest.mock import patch + +import pytest + from nadlan_mcp.fastmcp_server import ( autocomplete_address, - find_recent_deals_for_address, - get_street_deals, - get_neighborhood_deals, get_deals_by_radius, + get_neighborhood_deals, + get_street_deals, ) - +from nadlan_mcp.govmap.models import AutocompleteResponse, AutocompleteResult, Deal # Load fixtures FIXTURES_DIR = Path(__file__).parent / "fixtures" @@ -61,7 +62,7 @@ def mock_polygon_metadata_data(): class TestMCPToolsFast: """Fast unit tests for MCP tools using mocked data.""" - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_autocomplete_address_fast(self, mock_client, mock_autocomplete_data): """Test autocomplete with cached data.""" mock_client.autocomplete_address.return_value = mock_autocomplete_data @@ -74,7 +75,7 @@ class TestMCPToolsFast: assert "text" in data[0] assert "coordinates" in data[0] - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_get_street_deals_fast(self, mock_client, mock_street_deals_data): """Test street deals with cached data.""" mock_client.get_street_deals.return_value = mock_street_deals_data @@ -92,7 +93,7 @@ class TestMCPToolsFast: assert "deal_amount" in deal assert "deal_date" in deal - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_get_neighborhood_deals_fast(self, mock_client, mock_neighborhood_deals_data): """Test neighborhood deals with cached data.""" mock_client.get_neighborhood_deals.return_value = mock_neighborhood_deals_data @@ -104,7 +105,7 @@ class TestMCPToolsFast: assert data["total_deals"] == len(mock_neighborhood_deals_data) assert "deals" in data - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_get_deals_by_radius_fast(self, mock_client, mock_polygon_metadata_data): """Test deals by radius with cached data.""" mock_client.get_deals_by_radius.return_value = mock_polygon_metadata_data @@ -117,10 +118,15 @@ class TestMCPToolsFast: assert "polygons" in data @pytest.mark.skip(reason="Complex workflow with statistics - tested in E2E suite") - @patch('nadlan_mcp.fastmcp_server.client') - def test_find_recent_deals_fast(self, mock_client, mock_street_deals_data, - mock_neighborhood_deals_data, mock_polygon_metadata_data, - mock_autocomplete_data): + @patch("nadlan_mcp.fastmcp_server.client") + def test_find_recent_deals_fast( + self, + mock_client, + mock_street_deals_data, + mock_neighborhood_deals_data, + mock_polygon_metadata_data, + mock_autocomplete_data, + ): """Test find_recent_deals with fully mocked workflow. NOTE: This test is skipped because find_recent_deals_for_address has complex @@ -129,7 +135,7 @@ class TestMCPToolsFast: """ pass - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_no_deals_found(self, mock_client): """Test handling when no deals are found.""" mock_client.get_street_deals.return_value = [] @@ -140,7 +146,7 @@ class TestMCPToolsFast: assert isinstance(result, str) assert "No" in result or "found" in result.lower() - @patch('nadlan_mcp.fastmcp_server.client') + @patch("nadlan_mcp.fastmcp_server.client") def test_deal_type_filtering(self, mock_client, mock_street_deals_data): """Test that deal_type parameter is passed correctly.""" mock_client.get_street_deals.return_value = mock_street_deals_data diff --git a/tests/vcr_config.py b/tests/vcr_config.py index 54cb919..e28a6d6 100644 --- a/tests/vcr_config.py +++ b/tests/vcr_config.py @@ -10,26 +10,20 @@ import vcr # Configure VCR instance my_vcr = vcr.VCR( # Store cassettes in tests/cassettes/ directory - cassette_library_dir='tests/cassettes', - + cassette_library_dir="tests/cassettes", # Record mode: once = record once, then replay # Use 'new_episodes' to record new interactions but replay existing ones - record_mode='once', - + record_mode="once", # Match requests by method and URI - match_on=['method', 'scheme', 'host', 'port', 'path', 'query'], - + match_on=["method", "scheme", "host", "port", "path", "query"], # Filter out sensitive data from recordings - filter_headers=['authorization', 'x-api-key'], - + filter_headers=["authorization", "x-api-key"], # Decode compressed responses for better diffs decode_compressed_response=True, - # Serialize as YAML for human-readable diffs - serializer='yaml', - + serializer="yaml", # Path transformer to organize cassettes - path_transformer=vcr.VCR.ensure_suffix('.yaml'), + path_transformer=vcr.VCR.ensure_suffix(".yaml"), )