Complete Phase 2: Market Analysis, Filtering & Documentation

This commit implements all Phase 2 functionality with architectural
improvements over the original plan.

## Phase 2.1: Property Valuation Data ✅
- filter_deals_by_criteria() with comprehensive filtering
- calculate_deal_statistics() for statistical aggregations
- _extract_floor_number() for Hebrew floor parsing
- _calculate_std_dev() helper function
- MCP tools: get_valuation_comparables, get_deal_statistics

## Phase 2.2: Market Activity & Investment Analysis ✅
- calculate_market_activity_score() - deal frequency & velocity
  * Activity score (0-100), trend analysis, monthly distribution
  * Classifies markets: very_high, high, moderate, low, very_low
- analyze_investment_potential() - price trends & stability
  * Price appreciation rate via linear regression
  * Volatility score using coefficient of variation
  * Investment score combining appreciation & stability
- get_market_liquidity() - turnover & liquidity metrics
  * Quarterly/monthly breakdowns, velocity scoring
  * Trend direction, most active periods
- MCP tool: get_market_activity_metrics (unified tool)

## Phase 2.3: Enhanced Deal Filtering ✅
- Property type, room count, price, area, floor filtering
- All integrated into existing tools
- Hebrew floor number parsing support

## Testing ✅
- Added 15 comprehensive unit tests (all passing)
- Coverage: market activity, investment analysis, liquidity, filtering
- Edge cases: empty data, invalid dates, insufficient data

## Documentation ✅
- Created CLAUDE.md (~250 lines) - AI agent guidance
  * Development commands, architecture overview
  * Product vision from USECASES.md
  * Available tools with status indicators
- Updated TASKS.md - Phase 2 marked 100% complete

## Architectural Improvements
- 1 unified MCP tool instead of 6 separate tools (simpler API)
- 1 flexible filtering function instead of 3 (more composable)
- All logic in govmap.py (no new files, better cohesion)
- ~955 lines added with comprehensive documentation

## Design Principles Followed
✅ MCP provides data, LLM provides intelligence
✅ No predictions - only statistical calculations
✅ Comprehensive error handling & input validation
✅ Well-documented with detailed docstrings

Phase 2 Progress: 100% complete (60% overall project completion)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

nadlan_mcp/fastmcp_server.py

@@ -678,6 +678,82 @@ def get_deal_statistics(
         logger.error(f"Error in get_deal_statistics: {e}")
         return f"Error calculating deal statistics: {str(e)}"
 
+@mcp.tool()
+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
+    potential metrics. It combines activity scoring, liquidity analysis, and
+    investment potential into a single comprehensive report.
+
+    Args:
+        address: The address to analyze (in Hebrew or English)
+        years_back: How many years back to analyze (default: 2)
+        radius_meters: Search radius in meters (default: 100)
+
+    Returns:
+        JSON string containing:
+            - Market activity score and trends
+            - Market liquidity and velocity metrics
+            - Investment potential analysis
+            - Price appreciation and volatility
+    """
+    try:
+        # Get deals for the address
+        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)
+
+        # Calculate market activity score
+        try:
+            activity_metrics = client.calculate_market_activity_score(deals)
+        except ValueError as e:
+            activity_metrics = {"error": str(e)}
+
+        # Calculate market liquidity
+        try:
+            liquidity_metrics = client.get_market_liquidity(deals)
+        except ValueError as e:
+            liquidity_metrics = {"error": str(e)}
+
+        # Analyze investment potential
+        try:
+            investment_metrics = client.analyze_investment_potential(deals)
+        except ValueError as e:
+            investment_metrics = {"error": str(e)}
+
+        # 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_level": activity_metrics.get("activity_level", "unknown"),
+                "liquidity_rating": liquidity_metrics.get("liquidity_rating", "unknown"),
+                "investment_score": investment_metrics.get("investment_score", 0),
+                "price_trend": investment_metrics.get("price_trend", "unknown"),
+                "market_stability": investment_metrics.get("market_stability", "unknown")
+            }
+        }, 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()