The Deal model's 'rooms' field was missing the 'assetRoomNum' alias,
causing room data from the API to not be loaded into the model.
This resulted in all room-based filters returning 0 results.
Bug discovered when querying "חנקין 62 חולון" for 3-room apartments.
Query returned 0 results despite multiple 3-room deals existing in the data.
Root cause: Pydantic requires field aliases to map API field names (camelCase)
to Python attributes (snake_case). The 'rooms' field had no alias.
Fix: Added alias="assetRoomNum" to rooms field in Deal model.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Enables deployment to cloud platforms (Render, Railway, etc.) while maintaining
backward compatibility with existing stdio transport for Claude Desktop.
New features:
- HTTP server entry point (run_http_server.py) using uvicorn
- Docker containerization with Python 3.13
- Health check endpoint at /health
- Comprehensive deployment documentation for Render, Railway, and Docker
Technical changes:
- Added uvicorn dependency for ASGI server
- Created Dockerfile with optimized multi-stage build (343MB)
- Added .dockerignore for efficient Docker builds
- Implemented /health endpoint using Starlette JSONResponse
- Updated README.md and DEPLOYMENT.md with HTTP deployment guides
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Autocomplete returns different coordinates for vague queries (e.g. "תל אביב דיזנגוף"),
sometimes landing on points with no nearby polygon_ids within 30m radius.
- Try top 3 autocomplete results until one has polygons
- Fallback radius expansion (30m→200m) if no polygons found
- Fix limit validation bug (min 1 for deal queries)
Fixes: compare_addresses, get_valuation_comparables, get_deal_statistics
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
## Root Cause
After Pydantic migration, get_deals_by_radius() was attempting to validate
API responses as Deal objects. However, this endpoint returns POLYGON METADATA
(with fields: dealscount, polygon_id, settlementNameHeb), NOT individual deals.
All responses failed Pydantic validation (missing dealAmount, dealDate),
resulting in empty lists and 0 deals returned from ALL queries.
## Fixes Applied
### 1. client.py - get_deals_by_radius()
- Return type: `List[Deal]` → `List[Dict[str, Any]]`
- Remove Pydantic validation - return raw metadata dicts
- Update docstring to clarify this returns polygon metadata
- Add note to use find_recent_deals_for_address() for actual deals
### 2. client.py - find_recent_deals_for_address()
- Update to handle polygon metadata dicts (not Deal objects)
- Use dict.get('polygon_id') instead of model attribute access
- Rename variable: `nearby_deals` → `nearby_polygons` for clarity
### 3. fastmcp_server.py - get_deals_by_radius() tool
- Update to handle dict responses (not Deal objects)
- Remove strip_bloat_fields() call (not needed for metadata)
- Update docstring with WARNING about polygon metadata
- Change response keys: "deals" → "polygons", "total_deals" → "total_polygons"
### 4. Tests
- test_govmap_client.py: Update to expect dicts, not Deal objects
- test_fastmcp_tools.py: Update 3 tests to mock dict responses
## Impact
- ✅ find_recent_deals_for_address() NOW WORKS (was returning 0 deals)
- ✅ All 174 tests passing
- ✅ E2E API test confirmed working with real data
## API Behavior Documented
get_deals_by_radius endpoint design:
1. Returns polygon/area metadata (not individual deals)
2. Extract polygon_ids from metadata
3. Call get_street_deals(polygon_id) to get actual deals
4. This workflow is automated in find_recent_deals_for_address()
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixed all remaining test failures after Pydantic v2 migration:
Core fixes:
- Date handling: Convert date objects to ISO strings across 4 files
- Model serialization: Use model_dump(mode='json') for JSON compatibility
- Optional fields: Made time_period_months Optional[int] in models
- Dict access: Replace .get() with getattr() for dynamic attributes
Test updates:
- Updated 50+ test fixtures from dicts to Deal models
- Fixed date-based tests to use recent dates for time filtering
- Added missing imports (CoordinatePoint, MarketActivityScore, DealStatistics)
- Updated assertions from dict keys to model attributes (snake_case)
Files modified:
- nadlan_mcp/govmap/market_analysis.py
- nadlan_mcp/govmap/statistics.py
- nadlan_mcp/govmap/models.py
- nadlan_mcp/fastmcp_server.py
- tests/test_govmap_client.py
- tests/test_fastmcp_tools.py
- .cursor/plans/TEST-UPDATE-STATUS.md (comprehensive documentation)
Result: 174/174 tests passing (100%) ✅🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Bug Fix:
- Fixed autocomplete_address tool to use correct API response fields
- Changed from non-existent fields (addressLabel, settlementNameHeb, coordinates)
to actual API fields (text, id, type, score, shape)
- Added coordinate parsing from WKT POINT format
- Impact: HIGH - tool was returning empty data for all fields
Test Coverage Report:
- Added comprehensive test coverage analysis
- Documented 34 passing tests
- Identified e2e test results for MCP tools
- Listed missing test cases and recommendations
- Overall assessment: Good coverage, one bug found and fixed
E2E Test Results:
- ✅ find_recent_deals_for_address - WORKING
- ✅ analyze_market_trends - WORKING
- ✅ get_valuation_comparables - WORKING
- ✅ autocomplete_address - FIXED (needs MCP server restart to take effect)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
All functionality has been extracted to the govmap package:
- validators.py - Input validation functions
- utils.py - Utility functions
- filters.py - Deal filtering logic
- statistics.py - Statistical calculations
- market_analysis.py - Market analysis functions
- client.py - GovmapClient class with API methods
- __init__.py - Package exports
Backward compatibility maintained through package __init__.py exports.
Existing imports like `from nadlan_mcp.govmap import GovmapClient` now
use the govmap package instead of the old monolithic file.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Problem:
- get_valuation_comparables failed with 61,565 tokens (exceeded 25K MCP limit)
- Large MULTIPOLYGON shape data consumed ~40-50% of response tokens
- Not useful for LLM analysis, only bloating responses
Solution:
1. Added strip_bloat_fields() helper to remove:
- shape: Large coordinate data
- sourceorder: Internal ordering field
- source_polygon_id: Internal reference field
2. Applied to 5 MCP tools returning deal data:
- get_deals_by_radius
- get_street_deals
- find_recent_deals_for_address
- get_neighborhood_deals
- get_valuation_comparables
3. Reduced get_valuation_comparables default max_comparables: 200 → 50
Results:
- Token usage reduced by ~87% (61K → ~7-8K tokens)
- All tools now work within MCP token limits
- Cleaner, more efficient responses
- No loss of useful data for LLM analysis
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixed two critical bugs in filter_deals_by_criteria():
Bug #1: CRASH on None property type
- Issue: Code called .lower() on None when propertyTypeDescription was null
- Impact: Would crash MCP tool in production with incomplete data
- Fix: Check if deal_type is None/empty before calling .lower()
Bug #2: Missing data passes through filters
- Issue: Deals with None/missing values passed filters when they shouldn't
- Example: Filtering by area=60-70 would include deals with assetArea=None
- Impact: get_valuation_comparables returned inflated results with bad data
- Fix: Explicitly check for None when filter is active and exclude those deals
Changes:
- Property type filter: Check for None/empty before normalization
- Area filter: Exclude deals with missing area when min/max_area specified
- Room filter: Exclude deals with missing rooms when min/max_rooms specified
- Price filter: Exclude deals with missing price when min/max_price specified
- Invalid data: Changed from 'pass' to 'continue' to exclude bad data
Added 6 comprehensive unit tests:
- test_filter_excludes_missing_property_type
- test_filter_excludes_missing_area
- test_filter_excludes_missing_rooms
- test_filter_excludes_missing_price
- test_filter_excludes_invalid_numeric_data
- test_filter_allows_missing_data_when_no_filter
This fixes the E2E issue where get_valuation_comparables was returning
incomplete results. Now filters properly exclude deals with missing data.
Test results: 34/34 passing (6 new tests added)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fixes three critical bugs discovered during live testing with Claude:
Bug #1: Same building detection always returned 0
- Root cause: deal.get('address') returned empty string
- Fix: Construct address from streetNameHeb + houseNum fields
- Added test: test_same_building_detection_with_api_fields
Bug #2: Property type filter too strict
- Root cause: Exact match failed for variants like 'דירת גג'
- Fix: Use flexible substring matching with normalization
Bug #3: Deal deduplication used non-existent field
- Root cause: Deduplication key referenced deal.get('address')
- Fix: Removed non-existent field from deduplication key
Also added _calculate_distance() helper for future radius filtering.
Test results: 26/28 passing (2 pre-existing failures unrelated)
References: .cursor/plans/MCP-E2E-TEST-SUMMARY.md
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Refactor repetitive try-except blocks using _safe_calculate_metric helper
function to reduce code duplication and improve maintainability.
Changes:
- Add _safe_calculate_metric helper to centralize error handling
- Remove misleading default values (0/"unknown") in summary fields
- Use None defaults instead to clearly indicate unavailable data
- Add final newline to file per convention
This prevents LLMs from misinterpreting 0 as "zero investment potential"
when it actually means "data unavailable".
Addresses PR #2 review comments on lines 733, 749, and 759.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Extract duplicated date parsing logic into a reusable _parse_deal_dates
helper method. This centralizes error handling, validation, and date
grouping logic across multiple market analysis functions.
Key improvements:
- DRY: Eliminates code duplication across three functions
- Implements time_period_months filtering (fixes unused parameter bug)
- Returns both monthly and quarterly breakdowns in one pass
- Consistent error handling and logging
- Better maintainability
This change fixes the unused time_period_months parameters in both
calculate_market_activity_score and get_market_liquidity functions.
Addresses PR #2 review comments on lines 1013, 1058, and 1257.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Define module-level constants for market activity, volatility, and liquidity
thresholds. This improves code readability and makes threshold values easier
to maintain and adjust in the future.
Constants added:
- ACTIVITY_VERY_HIGH_THRESHOLD, ACTIVITY_HIGH_THRESHOLD, etc.
- VOLATILITY_VERY_VOLATILE_THRESHOLD, VOLATILITY_VOLATILE_THRESHOLD, etc.
- LIQUIDITY_VERY_HIGH_THRESHOLD, LIQUIDITY_HIGH_THRESHOLD, etc.
Addresses PR #2 review comments on lines 1084 and 1224.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Nitzan P