- Change ANALYSIS_IQR_MULTIPLIER default from 1.5 to 1.0 in config.py
- Add iqr_multiplier parameter to all filtering & statistics functions
- Allow runtime override via MCP tools (get_valuation_comparables, get_deal_statistics)
- Update CLAUDE.md docs with new default & override examples
Rationale: k=1.0 catches more suspicious deals (e.g. 43% below median) while
still preserving legitimate edge cases via hard bounds. Users can override
per-call for more conservative filtering (k=1.5) if needed.
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Co-Authored-By: Claude <noreply@anthropic.com>
Adds visibility into the deal filtering pipeline to help diagnose
why certain queries return fewer deals than expected.
New logs show:
1. Number of deals before criteria filtering
2. Number of deals after criteria filtering (with count removed)
3. Whether outlier filtering was applied
4. Number of deals after outlier filtering (with count removed)
5. Outlier filtering method and parameters used
Example output:
INFO: Applying criteria filters to 41 deals
INFO: After criteria filtering: 12 deals (removed 29 deals)
INFO: After outlier filtering (iqr, k=1.5): 4 deals (removed 8 outliers)
This helps users and developers understand:
- If criteria filters are too restrictive
- If outlier filtering is too aggressive
- Where deals are being filtered out in the pipeline
Addresses user question: "Why do I get just 4 deals for a central address
like סירקין 16 תל אביב?" - Now they can see exactly where deals were
filtered out.
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Co-Authored-By: Claude <noreply@anthropic.com>
The MCP now includes detailed outlier filtering information in the response,
allowing LLMs and bots to inform users about data quality improvements.
Changes to nadlan_mcp/fastmcp_server.py:
- Updated get_valuation_comparables to include outlier filtering metadata
- deal_breakdown now includes when filtering is applied:
- total_deals: Count after filtering (final result)
- total_deals_before_filtering: Original count before filtering
- outliers_removed: Number of deals filtered out
- filtering_method: Method used ("iqr", "percent", or "none")
- iqr_multiplier: IQR multiplier when using IQR method (e.g., 1.5)
- Updated docstring to clarify that outlier filtering is automatic
and metadata is included in response
Changes to tests/e2e/test_mcp_tools_comprehensive.py:
- Added assertions to verify outlier filtering metadata fields
- Test now validates the structure and types of filtering information
Example response:
{
"market_statistics": {
"deal_breakdown": {
"total_deals": 15,
"total_deals_before_filtering": 17,
"outliers_removed": 2,
"filtering_method": "iqr",
"iqr_multiplier": 1.5
}
}
}
This allows bots like nadlan-bot to properly inform users:
"Found 17 comparable deals, filtered 2 outliers using IQR method (k=1.5),
showing 15 deals."
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Co-Authored-By: Claude <noreply@anthropic.com>
Added log_mcp_call() helper function that logs all MCP tool invocations
with their parameters at INFO level. This provides:
- Better debugging and monitoring capabilities
- Clear visibility into which tools are being called and with what parameters
- Smart parameter formatting (truncates long strings, summarizes long lists)
- Consistent logging format across all 10 MCP tools
Example log output:
INFO - MCP tool called: find_recent_deals_for_address(address=סוקולוב 38 חולון, years_back=2, radius_meters=30, max_deals=100, deal_type=2)
This helps track API usage patterns and debug issues in production.
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Co-Authored-By: Claude <noreply@anthropic.com>
Problem:
- test_get_market_activity_metrics was failing with "list index out of range"
- _safe_calculate_metric only caught ValueError, not IndexError or other exceptions
- This caused the entire function to crash when metric calculations failed
Root Cause:
- Market metric functions (calculate_market_activity_score, get_market_liquidity,
analyze_investment_potential) can throw IndexError if insufficient data
- The helper function wasn't catching these exceptions
Solution:
- Changed _safe_calculate_metric to catch all exceptions (Exception instead of ValueError)
- Added logging to track which metric failed
- Now returns error dict gracefully instead of crashing
Impact:
- get_market_activity_metrics is now more robust
- Returns partial results even if some metrics fail
- All 326 tests now pass
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Co-Authored-By: Claude <noreply@anthropic.com>
Problem:
- get_valuation_comparables calculated statistics with outlier filtering
- But returned a deals list that still contained outliers (e.g., ₪900K, ₪1.3M deals)
- This caused inconsistency between statistics and the actual deals shown
Root Cause:
- calculate_deal_statistics() filters outliers internally for statistics
- But the deals array returned to user was only filtered by criteria (rooms, price, etc.)
- Outlier filtering was not applied to the returned deals list
Solution:
- Added explicit call to filter_deals_for_analysis() after filter_deals_by_criteria()
- Now both statistics AND deals list use outlier-filtered data
- Moved imports to top of file for better practice
Changes:
- nadlan_mcp/fastmcp_server.py:
- Added imports: get_config, filter_deals_for_analysis
- In get_valuation_comparables(): Apply outlier filtering before calculating stats
- Ensures deals list and statistics are consistent
Testing:
- 325/326 tests pass
- 1 unrelated test failure in get_market_activity_metrics (pre-existing issue)
After deployment, outliers will be automatically filtered from valuation comparables.
🤖 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>
## 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>
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>
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.
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Co-Authored-By: Claude <noreply@anthropic.com>
Nitzan Pomerantz