Complete Phase 4.1 test suite updates - all 174 tests passing

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>

nadlan_mcp/govmap/market_analysis.py

@@ -7,7 +7,7 @@ Focused on providing data metrics; the LLM interprets them for investment advice
 
 import logging
 from collections import defaultdict
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta
 from typing import Dict, List, Optional, Tuple
 
 from .models import Deal, MarketActivityScore, InvestmentAnalysis, LiquidityMetrics
@@ -68,11 +68,13 @@ def parse_deal_dates(
     deal_dates = []
 
     for deal in deals:
-        date_str = deal.deal_date
-        if not date_str:
+        if not deal.deal_date:
             continue
 
         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)
+
             # Filter by time period if specified
             if cutoff_date is not None and date_str < cutoff_date_str:
                 continue
@@ -100,7 +102,7 @@ def parse_deal_dates(
 
 
 def calculate_market_activity_score(
-    deals: List[Deal], time_period_months: int = 12
+    deals: List[Deal], time_period_months: Optional[int] = 12
 ) -> MarketActivityScore:
     """
     Calculate market activity and liquidity metrics.
@@ -209,10 +211,12 @@ def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis:
     price_data = []
     for deal in deals:
         price_per_sqm = deal.price_per_sqm  # Use computed field from Deal model
-        date_str = deal.deal_date
 
-        if price_per_sqm and price_per_sqm > 0 and date_str:
+        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)
+
                 # Parse date for sorting
                 year = int(date_str[:4])
                 month = int(date_str[5:7])
@@ -319,7 +323,7 @@ def analyze_investment_potential(deals: List[Deal]) -> InvestmentAnalysis:
 
 
 def get_market_liquidity(
-    deals: List[Deal], time_period_months: int = 12
+    deals: List[Deal], time_period_months: Optional[int] = 12
 ) -> LiquidityMetrics:
     """
     Get detailed market liquidity and turnover metrics.

nadlan_mcp/govmap/models.py

@@ -228,7 +228,7 @@ class MarketActivityScore(BaseModel):
     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: int = Field(..., description="Analysis period in months")
+    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)"
@@ -275,7 +275,7 @@ class LiquidityMetrics(BaseModel):
     """
     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: int = Field(..., description="Analysis period in months")
+    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)")

nadlan_mcp/govmap/statistics.py

@@ -121,16 +121,23 @@ def calculate_deal_statistics(deals: List[Deal]) -> DealStatistics:
     date_range_dict = None
     if deal_dates:
         try:
-            # Parse ISO date strings to get earliest and latest
+            # Convert dates to ISO strings for consistent formatting
+            from datetime import date as date_type
             parsed_dates = []
-            for date_str in deal_dates:
+            for d in deal_dates:
                 try:
-                    # 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]
-                    parsed_dates.append(date_str)
+                    # Handle date objects (from Pydantic models)
+                    if isinstance(d, date_type):
+                        parsed_dates.append(d.isoformat())
+                    else:
+                        # 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]
+                        parsed_dates.append(date_str)
                 except (ValueError, TypeError):
-                    logger.warning(f"Invalid date format: {date_str}")
+                    logger.warning(f"Invalid date format: {d}")
                     continue
 
             if parsed_dates:
@@ -140,7 +147,7 @@ def calculate_deal_statistics(deals: List[Deal]) -> DealStatistics:
                     "latest": sorted_dates[-1],
                 }
         except (ValueError, TypeError):
-            logger.warning("Invalid date format: {date_range_dict}")
+            logger.warning("Invalid date format in date range calculation")
             pass
 
     return DealStatistics(