Nitzan Pomerantz
5daaf70021
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 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
1176 lines
46 KiB
Python
1176 lines
46 KiB
Python
#!/usr/bin/env python3
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"""
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Simple FastMCP Server for Israeli Real Estate Data
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This server provides access to Israeli government real estate data through the Govmap API
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using the FastMCP library with simplified, working functions.
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"""
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import json
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import logging
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from typing import Any, Dict, List, Optional
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from mcp.server.fastmcp import FastMCP
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from starlette.responses import JSONResponse
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from nadlan_mcp.config import get_config
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from nadlan_mcp.govmap import GovmapClient
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from nadlan_mcp.govmap.models import Deal
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from nadlan_mcp.govmap.outlier_detection import filter_deals_for_analysis
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Initialize FastMCP server
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mcp = FastMCP("nadlan-mcp")
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# Initialize the Govmap client
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client = GovmapClient()
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def log_mcp_call(func_name: str, **params):
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"""
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Log MCP tool function calls with their parameters for debugging and monitoring.
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Args:
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func_name: Name of the MCP tool function being called
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**params: Keyword arguments passed to the function
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"""
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# Format parameters for logging (truncate long strings)
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formatted_params = {}
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for key, value in params.items():
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if isinstance(value, str) and len(value) > 100:
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formatted_params[key] = f"{value[:97]}..."
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elif isinstance(value, list) and len(value) > 5:
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formatted_params[key] = f"[{len(value)} items]"
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else:
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formatted_params[key] = value
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logger.info(
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f"MCP tool called: {func_name}({', '.join(f'{k}={v}' for k, v in formatted_params.items())})"
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)
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def strip_bloat_fields(deals: List[Deal]) -> List[Dict[str, Any]]:
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"""
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Remove bloat fields from Deal models to reduce token usage in MCP responses.
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Converts Deal models to dictionaries and removes:
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- shape: Large MULTIPOLYGON coordinate data (~40-50% of tokens, not useful for LLM analysis)
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- sourceorder: Internal ordering field
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- source_polygon_id: Internal reference field (only when it's a UUID string)
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Args:
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deals: List of Deal model instances
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Returns:
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List of deal dictionaries with bloat fields removed
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"""
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bloat_fields = {"shape", "sourceorder"}
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# Note: We keep source_polygon_id if it was added by our processing logic
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result = []
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for deal in deals:
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# Convert Deal model to dict, excluding None values for cleaner output
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# Use mode='json' to serialize dates as ISO strings
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deal_dict = deal.model_dump(mode="json", exclude_none=True)
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# Remove bloat fields
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filtered_dict = {k: v for k, v in deal_dict.items() if k not in bloat_fields}
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result.append(filtered_dict)
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return result
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@mcp.tool()
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def autocomplete_address(search_text: str) -> str:
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"""Search and autocomplete Israeli addresses.
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Args:
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search_text: The partial address to search for (in Hebrew or English)
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Returns:
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JSON string containing matching addresses with their coordinates
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"""
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log_mcp_call("autocomplete_address", search_text=search_text)
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try:
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response = client.autocomplete_address(search_text)
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if not response.results:
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return f"No addresses found for '{search_text}'"
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# Format results for better readability
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formatted_results = []
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for result in response.results:
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result_dict = {
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"text": result.text,
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"id": result.id,
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"type": result.type,
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"score": result.score,
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}
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# Add coordinates if available
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if result.coordinates:
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result_dict["coordinates"] = {
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"longitude": result.coordinates.longitude,
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"latitude": result.coordinates.latitude,
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}
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formatted_results.append(result_dict)
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return json.dumps(formatted_results, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in autocomplete_address: {e}", exc_info=True)
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return f"Error searching for address: {str(e)}"
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@mcp.tool()
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def get_deals_by_radius(latitude: float, longitude: float, radius_meters: int = 500) -> str:
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"""Get polygon metadata within a radius of coordinates.
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**NOTE**: This returns polygon/area metadata, NOT individual deal transactions!
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Use find_recent_deals_for_address() to get actual deals.
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Args:
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latitude: Latitude coordinate
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longitude: Longitude coordinate
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radius_meters: Search radius in meters (default: 500)
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Returns:
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JSON string containing polygon metadata (areas with deals nearby)
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"""
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log_mcp_call(
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"get_deals_by_radius", latitude=latitude, longitude=longitude, radius_meters=radius_meters
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)
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try:
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# Note: GovmapClient expects (longitude, latitude) tuple
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# Returns polygon metadata dicts, not Deal objects
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polygons = client.get_deals_by_radius((longitude, latitude), radius_meters)
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if not polygons:
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return f"No polygons found within {radius_meters}m of coordinates ({latitude}, {longitude})"
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return json.dumps(
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{
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"total_polygons": len(polygons),
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"search_radius_meters": radius_meters,
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"center_coordinates": {"latitude": latitude, "longitude": longitude},
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"polygons": polygons, # Return dicts directly, no stripping needed
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},
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ensure_ascii=False,
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indent=2,
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)
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except Exception as e:
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logger.error(f"Error in get_deals_by_radius: {e}", exc_info=True)
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return f"Error fetching polygons by radius: {str(e)}"
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@mcp.tool()
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def get_street_deals(polygon_id: str, limit: int = 100, deal_type: int = 2) -> str:
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"""Get real estate deals for a specific street polygon.
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Args:
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polygon_id: The polygon ID of the street/area
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limit: Maximum number of deals to return (default: 100)
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deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2)
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Returns:
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JSON string containing recent real estate deals for the street
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"""
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log_mcp_call("get_street_deals", polygon_id=polygon_id, limit=limit, deal_type=deal_type)
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try:
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deals = client.get_street_deals(polygon_id, limit, deal_type=deal_type)
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if not deals:
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deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
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return f"No {deal_type_desc} deals found for polygon ID {polygon_id}"
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# Add deal type metadata
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for deal in deals:
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deal.deal_type = deal_type
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deal.deal_type_description = "first_hand_new" if deal_type == 1 else "second_hand_used"
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# Calculate basic statistics using computed fields from models
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price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm]
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stats = {}
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if price_per_sqm_values:
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stats["price_per_sqm_stats"] = {
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"average_price_per_sqm": round(
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sum(price_per_sqm_values) / len(price_per_sqm_values), 0
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),
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"min_price_per_sqm": round(min(price_per_sqm_values), 0),
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"max_price_per_sqm": round(max(price_per_sqm_values), 0),
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}
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deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
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return json.dumps(
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{
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"total_deals": len(deals),
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"polygon_id": polygon_id,
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"deal_type": deal_type,
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"deal_type_description": deal_type_desc,
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"market_statistics": stats,
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"deals": strip_bloat_fields(deals),
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},
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ensure_ascii=False,
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indent=2,
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)
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except Exception as e:
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logger.error(f"Error in get_street_deals: {e}", exc_info=True)
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return f"Error fetching street deals: {str(e)}"
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@mcp.tool()
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def find_recent_deals_for_address(
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address: str,
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years_back: int = 2,
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radius_meters: int = 30,
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max_deals: int = 100,
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deal_type: int = 2,
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) -> str:
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"""Find recent real estate deals for a specific address.
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Args:
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address: The address to search for (in Hebrew or English)
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years_back: How many years back to search (default: 2)
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radius_meters: Search radius in meters from the address (default: 30)
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Small radius since street deals cover the entire street anyway
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max_deals: Maximum number of deals to return (default: 100, provides good context for LLM analysis)
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deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2)
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Returns:
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JSON string containing recent real estate deals for the address
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"""
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log_mcp_call(
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"find_recent_deals_for_address",
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address=address,
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years_back=years_back,
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radius_meters=radius_meters,
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max_deals=max_deals,
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deal_type=deal_type,
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)
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try:
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deals = client.find_recent_deals_for_address(
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address, years_back, radius_meters, max_deals, deal_type
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)
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if not deals:
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deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
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return f"No {deal_type_desc} deals found for address '{address}'"
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# Calculate comprehensive statistics using model attributes
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prices = [deal.deal_amount for deal in deals if deal.deal_amount]
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areas = [deal.asset_area for deal in deals if deal.asset_area]
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price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm]
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# Separate building, street and neighborhood deals for analysis
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# deal_source is added dynamically in find_recent_deals_for_address
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building_deals = [
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deal for deal in deals if getattr(deal, "deal_source", None) == "same_building"
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]
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street_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "street"]
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neighborhood_deals = [
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deal for deal in deals if getattr(deal, "deal_source", None) == "neighborhood"
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]
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stats = {
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"deal_breakdown": {
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"total_deals": len(deals),
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"same_building_deals": len(building_deals),
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"street_deals": len(street_deals),
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"neighborhood_deals": len(neighborhood_deals),
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"same_building_percentage": round((len(building_deals) / len(deals)) * 100, 1)
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if deals
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else 0,
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"street_emphasis_percentage": round((len(street_deals) / len(deals)) * 100, 1)
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if deals
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else 0,
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"neighborhood_percentage": round((len(neighborhood_deals) / len(deals)) * 100, 1)
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if deals
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else 0,
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}
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}
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# Standardize field names to match other tools
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if prices:
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stats["price_statistics"] = {
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"mean": round(sum(prices) / len(prices), 0),
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"min": min(prices),
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"max": max(prices),
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"median": sorted(prices)[len(prices) // 2] if prices else 0,
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"total": sum(prices),
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}
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if areas:
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stats["area_statistics"] = {
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"mean": round(sum(areas) / len(areas), 1),
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"min": min(areas),
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"max": max(areas),
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"median": sorted(areas)[len(areas) // 2] if areas else 0,
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}
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if price_per_sqm_values:
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stats["price_per_sqm_statistics"] = {
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"mean": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0),
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"min": round(min(price_per_sqm_values), 0),
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"max": round(max(price_per_sqm_values), 0),
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"median": round(sorted(price_per_sqm_values)[len(price_per_sqm_values) // 2], 0)
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if price_per_sqm_values
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else 0,
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}
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deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
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return json.dumps(
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{
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"search_parameters": {
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"address": address,
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"years_back": years_back,
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"radius_meters": radius_meters,
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"max_deals": max_deals,
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"deal_type": deal_type,
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"deal_type_description": deal_type_desc,
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},
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"market_statistics": stats,
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"deals": strip_bloat_fields(deals),
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},
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ensure_ascii=False,
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indent=2,
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)
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except Exception as e:
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logger.error(f"Error in find_recent_deals_for_address: {e}", exc_info=True)
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return f"Error analyzing address: {str(e)}"
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@mcp.tool()
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def get_neighborhood_deals(polygon_id: str, limit: int = 100, deal_type: int = 2) -> str:
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"""Get real estate deals for a specific neighborhood polygon.
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Args:
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polygon_id: The polygon ID of the neighborhood
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limit: Maximum number of deals to return (default: 100)
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deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2)
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Returns:
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JSON string containing recent real estate deals in the specified neighborhood
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"""
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log_mcp_call("get_neighborhood_deals", polygon_id=polygon_id, limit=limit, deal_type=deal_type)
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try:
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deals = client.get_neighborhood_deals(polygon_id, limit, deal_type=deal_type)
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if not deals:
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deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
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return f"No {deal_type_desc} deals found for polygon ID {polygon_id}"
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# Add deal type metadata
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for deal in deals:
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deal.deal_type = deal_type
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deal.deal_type_description = "first_hand_new" if deal_type == 1 else "second_hand_used"
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# Calculate basic statistics using computed fields from models
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price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm]
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stats = {}
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if price_per_sqm_values:
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stats["price_per_sqm_stats"] = {
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"average_price_per_sqm": round(
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sum(price_per_sqm_values) / len(price_per_sqm_values), 0
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),
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"min_price_per_sqm": round(min(price_per_sqm_values), 0),
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"max_price_per_sqm": round(max(price_per_sqm_values), 0),
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}
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deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
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return json.dumps(
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{
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"total_deals": len(deals),
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"polygon_id": polygon_id,
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"deal_type": deal_type,
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"deal_type_description": deal_type_desc,
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"market_statistics": stats,
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"deals": strip_bloat_fields(deals),
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},
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ensure_ascii=False,
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indent=2,
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)
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except Exception as e:
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logger.error(f"Error in get_neighborhood_deals: {e}", exc_info=True)
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return f"Error fetching neighborhood deals: {str(e)}"
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@mcp.tool()
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def analyze_market_trends(
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address: str,
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years_back: int = 3,
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radius_meters: int = 100,
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max_deals: int = 100,
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deal_type: int = 2,
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) -> str:
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"""Analyze market trends and price patterns for an area with comprehensive data.
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Args:
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address: The address to analyze trends around
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years_back: How many years of data to analyze (default: 3)
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radius_meters: Search radius in meters from the address (default: 100, larger for trend analysis)
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max_deals: Maximum number of deals to analyze (default: 100, optimized for performance and token limits)
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deal_type: Deal type filter (1=first hand/new, 2=second hand/used, default: 2)
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Returns:
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JSON string containing comprehensive market trend analysis (summarized data, not raw deals)
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"""
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log_mcp_call(
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"analyze_market_trends",
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address=address,
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years_back=years_back,
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radius_meters=radius_meters,
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max_deals=max_deals,
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deal_type=deal_type,
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)
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try:
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# Get deals for the address with larger radius for trend analysis
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deals = client.find_recent_deals_for_address(
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address, years_back, radius_meters, max_deals, deal_type
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)
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if not deals:
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deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
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return f"No {deal_type_desc} deals found for comprehensive market analysis near '{address}'"
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# Efficient analysis with reduced complexity
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from collections import defaultdict
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yearly_data = defaultdict(list)
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property_types: Dict[str, List[float]] = defaultdict(
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list
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) # Store only prices for efficiency
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neighborhoods = defaultdict(list)
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# Simplified processing - extract only essential data
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for deal in deals:
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if not deal.deal_date:
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continue
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|
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# Convert date to string for parsing
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from datetime import date as date_type
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date_str = (
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deal.deal_date.isoformat()
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if isinstance(deal.deal_date, date_type)
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else str(deal.deal_date)
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)
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year = date_str[:4]
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price = deal.deal_amount
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area = deal.asset_area
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price_per_sqm = deal.price_per_sqm
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prop_type = deal.property_type_description or "לא ידוע"
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neighborhood = deal.settlement_name_heb or deal.neighborhood or "לא ידוע"
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deal_source = getattr(deal, "deal_source", "unknown")
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|
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if (
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isinstance(price, (int, float))
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and isinstance(area, (int, float))
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and area > 0
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and isinstance(price_per_sqm, (int, float))
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):
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yearly_data[year].append(
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{
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"price": price,
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"area": area,
|
|
"price_per_sqm": price_per_sqm,
|
|
"deal_source": deal_source,
|
|
}
|
|
)
|
|
property_types[prop_type].append(price_per_sqm)
|
|
neighborhoods[neighborhood].append(price_per_sqm)
|
|
|
|
# Calculate streamlined yearly trends
|
|
yearly_trends = {}
|
|
for year, year_deals in yearly_data.items():
|
|
if year_deals:
|
|
prices = [d["price"] for d in year_deals]
|
|
price_per_sqm_vals = [d["price_per_sqm"] for d in year_deals]
|
|
building_deals = [d for d in year_deals if d["deal_source"] == "same_building"]
|
|
street_deals = [d for d in year_deals if d["deal_source"] == "street"]
|
|
|
|
yearly_trends[year] = {
|
|
"deal_count": len(year_deals),
|
|
"same_building_deals": len(building_deals),
|
|
"street_deals": len(street_deals),
|
|
"avg_price": round(sum(prices) / len(prices), 0),
|
|
"avg_price_per_sqm": round(
|
|
sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0
|
|
),
|
|
"min_price_per_sqm": round(min(price_per_sqm_vals), 0),
|
|
"max_price_per_sqm": round(max(price_per_sqm_vals), 0),
|
|
"total_volume": sum(prices),
|
|
}
|
|
|
|
# Streamlined property type analysis (top 5 only)
|
|
property_type_analysis = {}
|
|
for prop_type, prices_per_sqm in property_types.items():
|
|
if len(prices_per_sqm) >= 2: # Only include types with multiple deals
|
|
property_type_analysis[prop_type] = {
|
|
"deal_count": len(prices_per_sqm),
|
|
"avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0),
|
|
}
|
|
|
|
# Keep only top 5 property types by deal count
|
|
property_type_analysis = dict(
|
|
sorted(property_type_analysis.items(), key=lambda x: x[1]["deal_count"], reverse=True)[
|
|
:5
|
|
]
|
|
)
|
|
|
|
# Streamlined neighborhood analysis (top 5 only)
|
|
neighborhood_analysis = {}
|
|
for neighborhood, prices_per_sqm in neighborhoods.items():
|
|
if len(prices_per_sqm) >= 3: # Minimum 3 deals for statistical significance
|
|
neighborhood_analysis[neighborhood] = {
|
|
"deal_count": len(prices_per_sqm),
|
|
"avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0),
|
|
}
|
|
|
|
# Keep only top 5 neighborhoods by deal count
|
|
neighborhood_analysis = dict(
|
|
sorted(neighborhood_analysis.items(), key=lambda x: x[1]["deal_count"], reverse=True)[
|
|
:5
|
|
]
|
|
)
|
|
|
|
# Simple trend analysis
|
|
years_sorted = sorted(yearly_trends.keys())
|
|
trend_analysis = {}
|
|
if len(years_sorted) >= 2:
|
|
first_year = yearly_trends[years_sorted[0]]
|
|
last_year = yearly_trends[years_sorted[-1]]
|
|
|
|
if first_year["avg_price_per_sqm"] > 0:
|
|
price_change = (
|
|
(last_year["avg_price_per_sqm"] - first_year["avg_price_per_sqm"])
|
|
/ first_year["avg_price_per_sqm"]
|
|
) * 100
|
|
volume_change = (
|
|
(
|
|
(last_year["deal_count"] - first_year["deal_count"])
|
|
/ first_year["deal_count"]
|
|
)
|
|
* 100
|
|
if first_year["deal_count"] > 0
|
|
else 0
|
|
)
|
|
|
|
trend_analysis = {
|
|
"price_trend_percentage": round(price_change, 1),
|
|
"volume_trend_percentage": round(volume_change, 1),
|
|
"first_year_avg_price_per_sqm": first_year["avg_price_per_sqm"],
|
|
"last_year_avg_price_per_sqm": last_year["avg_price_per_sqm"],
|
|
}
|
|
|
|
deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
|
|
|
|
# Return summarized analysis (NO raw deals to save tokens)
|
|
# Normalize structure with standard market_statistics while keeping tool-specific analysis
|
|
return json.dumps(
|
|
{
|
|
"analysis_parameters": {
|
|
"address": address,
|
|
"years_analyzed": years_back,
|
|
"radius_meters": radius_meters,
|
|
"deals_analyzed": len(deals),
|
|
"deal_type": deal_type,
|
|
"deal_type_description": deal_type_desc,
|
|
},
|
|
"market_statistics": {
|
|
"deal_breakdown": {
|
|
"total_deals": len(deals),
|
|
},
|
|
},
|
|
"market_summary": {
|
|
"years_with_data": len(yearly_trends),
|
|
"unique_property_types": len(property_type_analysis),
|
|
"unique_neighborhoods": len(neighborhood_analysis),
|
|
},
|
|
"yearly_trends": yearly_trends,
|
|
"top_property_types": property_type_analysis,
|
|
"top_neighborhoods": neighborhood_analysis,
|
|
"trend_analysis": trend_analysis,
|
|
"key_insights": {
|
|
"most_active_year": max(
|
|
yearly_trends.keys(), key=lambda y: yearly_trends[y]["deal_count"]
|
|
)
|
|
if yearly_trends
|
|
else None,
|
|
"highest_avg_price_year": max(
|
|
yearly_trends.keys(), key=lambda y: yearly_trends[y]["avg_price_per_sqm"]
|
|
)
|
|
if yearly_trends
|
|
else None,
|
|
"deal_source_summary": f"Building: {len([d for d in deals if getattr(d, 'deal_source', None) == 'same_building'])}, Street: {len([d for d in deals if getattr(d, 'deal_source', None) == 'street'])}, Neighborhood: {len([d for d in deals if getattr(d, 'deal_source', None) == 'neighborhood'])}",
|
|
},
|
|
"deals": [], # Trend analysis doesn't return raw deals to save tokens
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in analyze_market_trends: {e}", exc_info=True)
|
|
return f"Error analyzing market trends: {str(e)}"
|
|
|
|
|
|
@mcp.tool()
|
|
def compare_addresses(addresses: List[str]) -> str:
|
|
"""Compare real estate markets between multiple addresses.
|
|
|
|
Args:
|
|
addresses: List of addresses to compare (in Hebrew or English)
|
|
|
|
Returns:
|
|
JSON string containing comparative analysis of multiple addresses
|
|
"""
|
|
log_mcp_call("compare_addresses", addresses=addresses)
|
|
try:
|
|
comparisons = []
|
|
|
|
for address in addresses:
|
|
try:
|
|
deals = client.find_recent_deals_for_address(address, 2)
|
|
|
|
if deals:
|
|
prices = [deal.deal_amount for deal in deals if deal.deal_amount]
|
|
areas = [deal.asset_area for deal in deals if deal.asset_area]
|
|
price_per_sqm_values = [
|
|
deal.price_per_sqm for deal in deals if deal.price_per_sqm
|
|
]
|
|
building_deals = [
|
|
deal
|
|
for deal in deals
|
|
if getattr(deal, "deal_source", None) == "same_building"
|
|
]
|
|
street_deals = [
|
|
deal for deal in deals if getattr(deal, "deal_source", None) == "street"
|
|
]
|
|
neighborhood_deals = [
|
|
deal
|
|
for deal in deals
|
|
if getattr(deal, "deal_source", None) == "neighborhood"
|
|
]
|
|
|
|
comparison = {
|
|
"address": address,
|
|
"total_deals": len(deals),
|
|
"same_building_deals": len(building_deals),
|
|
"street_deals": len(street_deals),
|
|
"neighborhood_deals": len(neighborhood_deals),
|
|
"same_building_percentage": round(
|
|
(len(building_deals) / len(deals)) * 100, 1
|
|
)
|
|
if deals
|
|
else 0,
|
|
"street_emphasis_percentage": round(
|
|
(len(street_deals) / len(deals)) * 100, 1
|
|
)
|
|
if deals
|
|
else 0,
|
|
"price_stats": {
|
|
"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
|
|
"min_price": min(prices) if prices else 0,
|
|
"max_price": max(prices) if prices else 0,
|
|
},
|
|
"area_stats": {
|
|
"average_area": round(sum(areas) / len(areas), 1) if areas else 0,
|
|
"min_area": min(areas) if areas else 0,
|
|
"max_area": max(areas) if areas else 0,
|
|
},
|
|
"price_per_sqm_stats": {
|
|
"average_price_per_sqm": round(
|
|
sum(price_per_sqm_values) / len(price_per_sqm_values), 0
|
|
)
|
|
if price_per_sqm_values
|
|
else 0,
|
|
"min_price_per_sqm": round(min(price_per_sqm_values), 0)
|
|
if price_per_sqm_values
|
|
else 0,
|
|
"max_price_per_sqm": round(max(price_per_sqm_values), 0)
|
|
if price_per_sqm_values
|
|
else 0,
|
|
},
|
|
}
|
|
else:
|
|
comparison = {
|
|
"address": address,
|
|
"total_deals": 0,
|
|
"same_building_deals": 0,
|
|
"street_deals": 0,
|
|
"neighborhood_deals": 0,
|
|
"same_building_percentage": 0,
|
|
"street_emphasis_percentage": 0,
|
|
"price_stats": {},
|
|
"area_stats": {},
|
|
"price_per_sqm_stats": {},
|
|
}
|
|
|
|
comparisons.append(comparison)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error comparing {address}: {e}")
|
|
comparisons.append({"address": address, "error": str(e)})
|
|
|
|
# Rank addresses by average price per sqm
|
|
valid_comparisons = []
|
|
for comparison in comparisons:
|
|
if (
|
|
isinstance(comparison, dict)
|
|
and "price_per_sqm_stats" in comparison
|
|
and isinstance(comparison["price_per_sqm_stats"], dict)
|
|
and comparison["price_per_sqm_stats"].get("average_price_per_sqm", 0) > 0
|
|
):
|
|
valid_comparisons.append(comparison)
|
|
|
|
# Sort by price per sqm
|
|
def get_price_per_sqm(comp: dict) -> float:
|
|
price_stats = comp.get("price_per_sqm_stats", {})
|
|
if isinstance(price_stats, dict):
|
|
return price_stats.get("average_price_per_sqm", 0)
|
|
return 0
|
|
|
|
valid_comparisons.sort(key=get_price_per_sqm, reverse=True)
|
|
|
|
return json.dumps(
|
|
{
|
|
"addresses_compared": len(addresses),
|
|
"ranking_by_average_price_per_sqm": valid_comparisons,
|
|
"all_results": comparisons,
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in compare_addresses: {e}", exc_info=True)
|
|
return f"Error comparing addresses: {str(e)}"
|
|
|
|
|
|
@mcp.tool()
|
|
def get_valuation_comparables(
|
|
address: str,
|
|
years_back: int = 2,
|
|
property_type: Optional[str] = None,
|
|
min_rooms: Optional[float] = None,
|
|
max_rooms: Optional[float] = None,
|
|
min_price: Optional[float] = None,
|
|
max_price: Optional[float] = None,
|
|
min_area: Optional[float] = None,
|
|
max_area: Optional[float] = None,
|
|
min_floor: Optional[int] = None,
|
|
max_floor: Optional[int] = None,
|
|
radius_meters: int = 100,
|
|
max_comparables: int = 50,
|
|
iqr_multiplier: Optional[float] = None,
|
|
) -> str:
|
|
"""Get comparable properties for valuation analysis.
|
|
|
|
This tool provides detailed comparable deals filtered by your criteria.
|
|
Automatically applies IQR outlier filtering (k=1.0 default) to remove statistical outliers
|
|
and improve data quality. The response includes metadata about filtering so you can
|
|
inform users about removed outliers.
|
|
|
|
Args:
|
|
address: The address to find comparables for (in Hebrew or English)
|
|
years_back: How many years back to search (default: 2)
|
|
property_type: Filter by property type (e.g., "דירה", "בית", "פנטהאוז")
|
|
min_rooms: Minimum number of rooms
|
|
max_rooms: Maximum number of rooms
|
|
min_price: Minimum deal amount (NIS)
|
|
max_price: Maximum deal amount (NIS)
|
|
min_area: Minimum asset area (square meters)
|
|
max_area: Maximum asset area (square meters)
|
|
min_floor: Minimum floor number
|
|
max_floor: Maximum floor number
|
|
radius_meters: Search radius in meters (default: 100, larger than find_recent_deals to get more comparables)
|
|
max_comparables: Maximum number of deals to return (default: 50, optimized for MCP token limits)
|
|
iqr_multiplier: Override IQR multiplier for outlier detection (default: 1.0). Lower = more aggressive filtering
|
|
|
|
Returns:
|
|
JSON string containing:
|
|
- Filtered comparable deals with full details
|
|
- deal_breakdown with outlier filtering metadata:
|
|
- total_deals: Count after filtering
|
|
- total_deals_before_filtering: Count before filtering
|
|
- outliers_removed: Number of deals filtered out
|
|
- filtering_method: Method used (e.g., "iqr")
|
|
- iqr_multiplier: IQR multiplier used (e.g., 1.0)
|
|
"""
|
|
log_mcp_call(
|
|
"get_valuation_comparables",
|
|
address=address,
|
|
years_back=years_back,
|
|
property_type=property_type,
|
|
min_rooms=min_rooms,
|
|
max_rooms=max_rooms,
|
|
min_price=min_price,
|
|
max_price=max_price,
|
|
min_area=min_area,
|
|
max_area=max_area,
|
|
min_floor=min_floor,
|
|
max_floor=max_floor,
|
|
radius_meters=radius_meters,
|
|
max_comparables=max_comparables,
|
|
iqr_multiplier=iqr_multiplier,
|
|
)
|
|
try:
|
|
# Get all deals for the address with higher limits for valuation
|
|
deals = client.find_recent_deals_for_address(
|
|
address, years_back, radius=radius_meters, max_deals=max_comparables
|
|
)
|
|
|
|
if not deals:
|
|
return json.dumps(
|
|
{
|
|
"search_parameters": {
|
|
"address": address,
|
|
"years_back": years_back,
|
|
"radius_meters": radius_meters,
|
|
"max_comparables": max_comparables,
|
|
},
|
|
"market_statistics": {
|
|
"deal_breakdown": {
|
|
"total_deals": 0,
|
|
},
|
|
},
|
|
"deals": [],
|
|
"message": "No deals found for this address",
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
# Apply filters
|
|
logger.info(f"Applying criteria filters to {len(deals)} deals")
|
|
filtered_deals = client.filter_deals_by_criteria(
|
|
deals,
|
|
property_type=property_type,
|
|
min_rooms=min_rooms,
|
|
max_rooms=max_rooms,
|
|
min_price=min_price,
|
|
max_price=max_price,
|
|
min_area=min_area,
|
|
max_area=max_area,
|
|
min_floor=min_floor,
|
|
max_floor=max_floor,
|
|
)
|
|
logger.info(
|
|
f"After criteria filtering: {len(filtered_deals)} deals "
|
|
f"(removed {len(deals) - len(filtered_deals)} deals)"
|
|
)
|
|
|
|
# Apply outlier filtering to remove statistical outliers
|
|
config = get_config()
|
|
outlier_report = None
|
|
if (
|
|
config.analysis_outlier_method != "none"
|
|
and len(filtered_deals) >= config.analysis_min_deals_for_outlier_detection
|
|
):
|
|
deals_before_outlier_filter = len(filtered_deals)
|
|
filtered_deals, outlier_report = filter_deals_for_analysis(
|
|
filtered_deals, config, metric="price_per_sqm", iqr_multiplier=iqr_multiplier
|
|
)
|
|
effective_k = (
|
|
iqr_multiplier if iqr_multiplier is not None else config.analysis_iqr_multiplier
|
|
)
|
|
logger.info(
|
|
f"After outlier filtering ({config.analysis_outlier_method}, k={effective_k}): "
|
|
f"{len(filtered_deals)} deals (removed {deals_before_outlier_filter - len(filtered_deals)} outliers)"
|
|
)
|
|
else:
|
|
logger.info(
|
|
f"Skipping outlier filtering: only {len(filtered_deals)} deals "
|
|
f"(minimum {config.analysis_min_deals_for_outlier_detection} required)"
|
|
)
|
|
|
|
# Calculate statistics on filtered comparables
|
|
stats = client.calculate_deal_statistics(filtered_deals)
|
|
|
|
# Build deal breakdown with outlier filtering information
|
|
deal_breakdown = {
|
|
"total_deals": len(filtered_deals),
|
|
}
|
|
|
|
# Add outlier filtering metadata if filtering was applied
|
|
if outlier_report:
|
|
deal_breakdown["total_deals_before_filtering"] = outlier_report["total_deals"]
|
|
deal_breakdown["outliers_removed"] = outlier_report["outliers_removed"]
|
|
deal_breakdown["filtering_method"] = outlier_report["method_used"]
|
|
if outlier_report["method_used"] == "iqr":
|
|
deal_breakdown["iqr_multiplier"] = outlier_report["parameters"]["iqr_multiplier"]
|
|
|
|
# Normalize response structure to match other tools
|
|
return json.dumps(
|
|
{
|
|
"search_parameters": {
|
|
"address": address,
|
|
"years_back": years_back,
|
|
"radius_meters": radius_meters,
|
|
"max_comparables": max_comparables,
|
|
"filters_applied": {
|
|
"property_type": property_type,
|
|
"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
|
|
"price": f"{min_price}-{max_price}" if min_price or max_price else None,
|
|
"area": f"{min_area}-{max_area}" if min_area or max_area else None,
|
|
"floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None,
|
|
},
|
|
},
|
|
"market_statistics": {
|
|
"deal_breakdown": deal_breakdown,
|
|
"price_statistics": stats.price_statistics,
|
|
"area_statistics": stats.area_statistics,
|
|
"price_per_sqm_statistics": stats.price_per_sqm_statistics,
|
|
"property_type_distribution": stats.property_type_distribution,
|
|
"date_range": stats.date_range,
|
|
},
|
|
"deals": strip_bloat_fields(filtered_deals),
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in get_valuation_comparables: {e}", exc_info=True)
|
|
return f"Error getting valuation comparables: {str(e)}"
|
|
|
|
|
|
@mcp.tool()
|
|
def get_deal_statistics(
|
|
address: str,
|
|
years_back: int = 2,
|
|
property_type: Optional[str] = None,
|
|
min_rooms: Optional[float] = None,
|
|
max_rooms: Optional[float] = None,
|
|
iqr_multiplier: Optional[float] = None,
|
|
) -> str:
|
|
"""Calculate statistical aggregations on deal data for an address.
|
|
|
|
This tool provides quick statistical summaries without returning all raw deals.
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Useful when LLM needs calculations on large datasets without full details.
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|
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|
Args:
|
|
address: The address to analyze (in Hebrew or English)
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|
years_back: How many years back to analyze (default: 2)
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|
property_type: Filter by property type (e.g., "דירה", "בית")
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|
min_rooms: Minimum number of rooms
|
|
max_rooms: Maximum number of rooms
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|
iqr_multiplier: Override IQR multiplier for outlier detection (default: 1.0). Lower = more aggressive filtering
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|
|
|
Returns:
|
|
JSON string containing statistical metrics (mean, median, percentiles, etc.)
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|
"""
|
|
log_mcp_call(
|
|
"get_deal_statistics",
|
|
address=address,
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|
years_back=years_back,
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|
property_type=property_type,
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|
min_rooms=min_rooms,
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|
max_rooms=max_rooms,
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|
iqr_multiplier=iqr_multiplier,
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|
)
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|
try:
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|
# Get all deals for the address
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|
deals = client.find_recent_deals_for_address(address, years_back)
|
|
|
|
if not deals:
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|
return json.dumps(
|
|
{
|
|
"search_parameters": {
|
|
"address": address,
|
|
"years_back": years_back,
|
|
},
|
|
"market_statistics": {
|
|
"deal_breakdown": {"total_deals": 0},
|
|
"message": "No deals found for this address",
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|
},
|
|
"deals": [],
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
# Apply filters if provided
|
|
if property_type or min_rooms or max_rooms:
|
|
deals = client.filter_deals_by_criteria(
|
|
deals, property_type=property_type, min_rooms=min_rooms, max_rooms=max_rooms
|
|
)
|
|
|
|
# Calculate statistics
|
|
stats = client.calculate_deal_statistics(deals, iqr_multiplier=iqr_multiplier)
|
|
|
|
# Normalize response structure to match other tools
|
|
return json.dumps(
|
|
{
|
|
"search_parameters": {
|
|
"address": address,
|
|
"years_back": years_back,
|
|
"filters_applied": {
|
|
"property_type": property_type,
|
|
"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
|
|
},
|
|
},
|
|
"market_statistics": {
|
|
"deal_breakdown": {
|
|
"total_deals": stats.total_deals,
|
|
},
|
|
"price_statistics": stats.price_statistics,
|
|
"area_statistics": stats.area_statistics,
|
|
"price_per_sqm_statistics": stats.price_per_sqm_statistics,
|
|
"property_type_distribution": stats.property_type_distribution,
|
|
"date_range": stats.date_range,
|
|
},
|
|
"deals": [], # Statistics-only query, no full deals returned
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in get_deal_statistics: {e}", exc_info=True)
|
|
return f"Error calculating deal statistics: {str(e)}"
|
|
|
|
|
|
def _safe_calculate_metric(metric_func, deals):
|
|
"""
|
|
Safely execute a metric calculation function.
|
|
|
|
Helper function to reduce code duplication in try-except blocks
|
|
for market metric calculations.
|
|
|
|
Args:
|
|
metric_func: Function to call with deals as argument
|
|
deals: List of Deal model instances to analyze
|
|
|
|
Returns:
|
|
Result dictionary from metric_func (serialized from Pydantic model),
|
|
or error dictionary if any exception raised
|
|
"""
|
|
try:
|
|
result = metric_func(deals)
|
|
# Serialize Pydantic model to dict
|
|
if hasattr(result, "model_dump"):
|
|
return result.model_dump(exclude_none=True)
|
|
return result
|
|
except Exception as e:
|
|
logger.warning(f"Error calculating metric {metric_func.__name__}: {e}")
|
|
return {"error": 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
|
|
"""
|
|
log_mcp_call(
|
|
"get_market_activity_metrics",
|
|
address=address,
|
|
years_back=years_back,
|
|
radius_meters=radius_meters,
|
|
)
|
|
try:
|
|
# Get deals for the address
|
|
deals = client.find_recent_deals_for_address(address, years_back, radius_meters)
|
|
|
|
if not deals:
|
|
return json.dumps(
|
|
{
|
|
"analysis_parameters": {
|
|
"address": address,
|
|
"years_back": years_back,
|
|
"radius_meters": radius_meters,
|
|
},
|
|
"market_statistics": {
|
|
"deal_breakdown": {
|
|
"total_deals": 0,
|
|
},
|
|
},
|
|
"deals": [],
|
|
"error": "No deals found for analysis",
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
# Calculate market metrics using helper to reduce duplication
|
|
activity_metrics = _safe_calculate_metric(client.calculate_market_activity_score, deals)
|
|
liquidity_metrics = _safe_calculate_metric(client.get_market_liquidity, deals)
|
|
investment_metrics = _safe_calculate_metric(client.analyze_investment_potential, deals)
|
|
|
|
# Combine all metrics with normalized structure
|
|
return json.dumps(
|
|
{
|
|
"analysis_parameters": {
|
|
"address": address,
|
|
"years_back": years_back,
|
|
"radius_meters": radius_meters,
|
|
},
|
|
"market_statistics": {
|
|
"deal_breakdown": {
|
|
"total_deals": len(deals),
|
|
},
|
|
},
|
|
"market_activity": activity_metrics,
|
|
"market_liquidity": liquidity_metrics,
|
|
"investment_potential": investment_metrics,
|
|
"summary": {
|
|
"activity_score": activity_metrics.get("activity_score"),
|
|
"activity_trend": activity_metrics.get("trend"),
|
|
"liquidity_score": liquidity_metrics.get("liquidity_score"),
|
|
"market_activity_level": liquidity_metrics.get("market_activity_level"),
|
|
"investment_score": investment_metrics.get("investment_score"),
|
|
"price_trend": investment_metrics.get("price_trend"),
|
|
"market_stability": investment_metrics.get("market_stability"),
|
|
},
|
|
"deals": [], # Activity metrics don't return raw deals
|
|
},
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in get_market_activity_metrics: {e}", exc_info=True)
|
|
return f"Error analyzing market activity: {str(e)}"
|
|
|
|
|
|
# Health check endpoint for HTTP deployments
|
|
@mcp.custom_route("/health", methods=["GET"])
|
|
async def health_check(request):
|
|
"""
|
|
Health check endpoint for container orchestration platforms (Render, Railway, etc.).
|
|
|
|
Returns a simple OK status to indicate the server is running.
|
|
This endpoint is accessible at GET /health when using HTTP transport.
|
|
"""
|
|
return JSONResponse({"status": "ok", "service": "nadlan-mcp"})
|
|
|
|
|
|
# Run the server
|
|
if __name__ == "__main__":
|
|
mcp.run()
|