Bug #1: Fixed IndexError in outlier_detection.py:256
- Missing enumerate() caused stale loop var to access beyond bounds
- Occurred when hard bounds filtered deals before IQR processing
- Added test reproducing exact scenario (21 deals → 8 filtered)
Bug #2: Added stack traces to all MCP tool error logs
- Added exc_info=True to 10 MCP tools' error handlers
- Improves debugging by logging full stack traces
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Co-Authored-By: Claude <noreply@anthropic.com>
The test_market_activity_trend_stable test was failing intermittently
because it used get_recent_date() which approximates months as 30 days,
causing inconsistent month boundary calculations depending on when the
test runs.
Root cause:
- Using months_ago * 30 creates shifting month boundaries
- Calendar dates don't align consistently with 30-day periods
- Deals could fall into unexpected months based on test execution date
- Trend calculation compares first half vs second half of months
- Inconsistent month grouping led to "decreasing" instead of "stable"
Solution:
- Use explicit, deterministic dates (15th of each month)
- Calculate year-month combinations going back from current month
- Ensures exactly 1 deal per month for 12 consecutive months
- All deals guaranteed to be within time_period_months=12 window
- Month boundaries are now consistent regardless of test execution date
Before: Test result varied based on current date (flaky)
After: Test result is always "stable" (deterministic)
This ensures the test validates the trend detection logic without
temporal flakiness.
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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>
Implement configurable outlier detection and robust statistical measures to
improve analysis accuracy for real estate data. Addresses issues with data
entry errors, partial deals, and other anomalies that skew statistics.
Key Features:
- IQR-based outlier detection (moderate filtering by default, k=1.5)
- Hard bounds filtering for obvious errors (price_per_sqm, deal_amount)
- Robust volatility using IQR instead of std_dev for investment analysis
- Transparent reporting with both filtered and unfiltered statistics
Implementation:
- Add outlier_detection.py module with IQR/percent/hard bounds methods
- Add OutlierReport model and enhance DealStatistics with filtered fields
- Update calculate_deal_statistics() to support optional outlier filtering
- Update analyze_investment_potential() to use robust volatility
- Add 9 new configuration parameters for customization
- Add comprehensive test suite (24 tests) for outlier detection
- Update CLAUDE.md with usage documentation
Configuration (all via env vars):
- ANALYSIS_OUTLIER_METHOD=iqr (default, or percent/none)
- ANALYSIS_IQR_MULTIPLIER=1.5 (moderate, 3.0=conservative)
- ANALYSIS_PRICE_PER_SQM_MIN/MAX=1000/100000 (bounds in NIS/sqm)
- ANALYSIS_MIN_DEAL_AMOUNT=100000 (catches partial deals)
- ANALYSIS_USE_ROBUST_VOLATILITY=true (IQR-based CV)
- ANALYSIS_USE_ROBUST_TRENDS=true (filter before regression)
Testing:
- All existing tests pass (326 passed)
- 24 new comprehensive outlier detection tests
- Real-world scenario tests (partial deals, data errors)
Backward Compatible:
- Default behavior improves accuracy without breaking changes
- All new fields in models are optional
- Config parameters have sensible defaults
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Co-Authored-By: Claude <noreply@anthropic.com>
- Remove requirements.txt and requirements-dev.txt (duplicates pyproject.toml)
- Update Dockerfile to use 'pip install .' instead of requirements.txt
- Update README.md and GitHub workflows to use 'pip install -e .[dev]'
- Fix test_get_valuation_comparables: check for 'deal_date' instead of non-existent 'asset_room_num'
(rooms field is optional and excluded when None due to exclude_none=True)
- Fix fastmcp version constraint in pyproject.toml (>=2.13.0,<3.0.0)
- Add vcrpy to dev dependencies
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Adjusted sample_deals fixture to ensure 5 deals across 5 distinct months.
Previous dates collapsed into 3 months due to naive 30-day calculations.
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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>
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>
Fixed both failing tests for 100% test coverage (28/28 passing):
Test 1: test_client_initialization_with_custom_url
- Issue: Test was passing string to GovmapClient constructor
- Fix: Import GovmapConfig and create proper config object
- Changes: Added import and wrapped custom_url in GovmapConfig
Test 2: test_find_recent_deals_for_address_integration
- Issue: Test expected date-first sorting, but function sorts by priority first
- Fix: Updated test expectations to match actual business logic
- Behavior: Results sorted by source priority (building > street > neighborhood), then date
- Changes: Added priority field to mock data and fixed assertions
All 28 tests now passing ✅🤖 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
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Co-Authored-By: Claude <noreply@anthropic.com>
Update tests to pass time_period_months=None for tests using old dates
(2022-2023) to disable time filtering. This is necessary because the
time_period_months parameter is now properly implemented and filters
out old deals by default.
Tests fixed:
- test_calculate_market_activity_score_success
- test_calculate_market_activity_score_high_activity
- test_get_market_liquidity_success
- test_get_market_liquidity_quarterly_breakdown
All market analysis tests now pass (25/27 tests passing, 2 pre-existing
failures unrelated to our changes).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Replace broad assertion (mean > 0) with specific expected value using
pytest.approx for floating-point comparison. This makes the test more
robust and prevents future regressions.
Changes:
- Change area_stats mean assertion from > 0 to == pytest.approx(80.0)
- Add final newline to file per convention
Expected mean: (80 + 90 + 70) / 3 = 80.0
Addresses PR #2 review comments on line 527.
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Co-Authored-By: Claude <noreply@anthropic.com>
Nitzan Pomerantz