Nitzan Pomerantz
417 lines
15 KiB
Python
417 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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FastMCP Server for Israeli Real Estate Data (Nadlan)
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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 for better compatibility and reliability.
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"""
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import logging
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from typing import List, Dict, Any, Optional
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from mcp.server.fastmcp import FastMCP
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from .main import GovmapClient # type: ignore
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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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@mcp.tool()
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async 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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try:
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response = client.autocomplete_address(search_text)
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if not response or 'results' not in response:
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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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formatted_results.append({
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"address": result.get("addressLabel", ""),
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"settlement": result.get("settlementNameHeb", ""),
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"coordinates": result.get("coordinates", {}),
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"polygon_id": result.get("polygon_id")
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})
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import json
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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}")
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return f"Error searching for address: {str(e)}"
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@mcp.tool()
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async def get_deals_by_radius(
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latitude: float,
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longitude: float,
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radius_meters: int = 500,
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limit: int = 100
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) -> str:
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"""Get real estate deals within a radius of coordinates.
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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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limit: Maximum number of deals to return (default: 100)
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Returns:
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JSON string containing recent real estate deals in the area
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"""
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try:
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deals = client.get_deals_by_radius((longitude, latitude), radius_meters)
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if not deals:
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return f"No deals found within {radius_meters}m of coordinates ({latitude}, {longitude})"
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# Format deals for better readability
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formatted_deals = []
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for deal in deals:
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formatted_deals.append({
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"address": deal.get("addressLabel", ""),
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"settlement": deal.get("settlementNameHeb", ""),
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"deal_amount": deal.get("dealAmount"),
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"deal_date": deal.get("dealDate", ""),
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"asset_area": deal.get("assetArea"),
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"asset_type": deal.get("assetTypeHeb", ""),
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"coordinates": deal.get("coordinates", {})
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})
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import json
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return json.dumps({
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"total_deals": len(formatted_deals),
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"search_radius_meters": radius_meters,
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"center_coordinates": {"latitude": latitude, "longitude": longitude},
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"deals": formatted_deals
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}, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in get_deals_by_radius: {e}")
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return f"Error fetching deals by radius: {str(e)}"
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@mcp.tool()
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async def get_street_deals(street_name: str, settlement_name: str, limit: int = 100) -> str:
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"""Get real estate deals for a specific street.
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Args:
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street_name: Name of the street (in Hebrew)
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settlement_name: Name of the city/settlement (in Hebrew)
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limit: Maximum number of deals to return (default: 100)
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Returns:
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JSON string containing recent real estate deals on the specified street
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"""
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try:
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deals = await client.get_street_deals(street_name, settlement_name, limit)
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if not deals:
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return f"No deals found for {street_name}, {settlement_name}"
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# Format deals for better readability
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formatted_deals = []
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for deal in deals:
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formatted_deals.append({
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"address": deal.get("addressLabel", ""),
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"settlement": deal.get("settlementNameHeb", ""),
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"deal_amount": deal.get("dealAmount"),
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"deal_date": deal.get("dealDate", ""),
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"asset_area": deal.get("assetArea"),
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"asset_type": deal.get("assetTypeHeb", ""),
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"coordinates": deal.get("coordinates", {})
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})
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import json
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return json.dumps({
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"total_deals": len(formatted_deals),
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"street": street_name,
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"settlement": settlement_name,
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"deals": formatted_deals
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}, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in get_street_deals: {e}")
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return f"Error fetching street deals: {str(e)}"
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@mcp.tool()
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async def get_neighborhood_deals(polygon_id: str, limit: int = 100) -> 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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Returns:
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JSON string containing recent real estate deals in the specified neighborhood
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"""
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try:
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deals = await client.get_neighborhood_deals(polygon_id, limit)
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if not deals:
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return f"No deals found for polygon ID {polygon_id}"
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# Format deals for better readability
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formatted_deals = []
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for deal in deals:
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formatted_deals.append({
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"address": deal.get("addressLabel", ""),
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"settlement": deal.get("settlementNameHeb", ""),
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"deal_amount": deal.get("dealAmount"),
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"deal_date": deal.get("dealDate", ""),
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"asset_area": deal.get("assetArea"),
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"asset_type": deal.get("assetTypeHeb", ""),
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"coordinates": deal.get("coordinates", {})
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})
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import json
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return json.dumps({
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"total_deals": len(formatted_deals),
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"polygon_id": polygon_id,
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"deals": formatted_deals
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}, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in get_neighborhood_deals: {e}")
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return f"Error fetching neighborhood deals: {str(e)}"
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@mcp.tool()
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async def find_recent_deals_for_address(
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address: str,
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radius_meters: int = 500,
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limit: int = 100
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) -> str:
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"""Comprehensive analysis of recent real estate deals for an address.
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This is the main tool for getting detailed market analysis around a specific address.
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It finds the coordinates for the address and then searches for deals in the area.
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Args:
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address: The address to analyze (in Hebrew or English)
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radius_meters: Search radius in meters (default: 500)
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limit: Maximum number of deals to return (default: 100)
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Returns:
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JSON string containing comprehensive real estate analysis including:
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- Address details and coordinates
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- Recent deals in the area
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- Market statistics and trends
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"""
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try:
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deals = await client.find_recent_deals_for_address(address, radius_meters, limit)
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if not deals:
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return f"No deals found for address '{address}'"
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# Calculate market statistics
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prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
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areas = [deal.get("assetArea", 0) for deal in deals if deal.get("assetArea")]
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stats = {}
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if prices:
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stats["price_stats"] = {
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"average_price": sum(prices) / len(prices),
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"min_price": min(prices),
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"max_price": max(prices),
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"total_deals": len(prices)
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}
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if areas:
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stats["area_stats"] = {
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"average_area": sum(areas) / len(areas),
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"min_area": min(areas),
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"max_area": max(areas)
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}
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# Format deals for better readability
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formatted_deals = []
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for deal in deals:
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formatted_deals.append({
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"address": deal.get("addressLabel", ""),
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"settlement": deal.get("settlementNameHeb", ""),
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"deal_amount": deal.get("dealAmount"),
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"deal_date": deal.get("dealDate", ""),
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"asset_area": deal.get("assetArea"),
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"asset_type": deal.get("assetTypeHeb", ""),
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"coordinates": deal.get("coordinates", {})
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})
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import json
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return json.dumps({
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"search_address": address,
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"search_radius_meters": radius_meters,
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"total_deals": len(formatted_deals),
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"market_statistics": stats,
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"deals": formatted_deals
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}, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in find_recent_deals_for_address: {e}")
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return f"Error analyzing address: {str(e)}"
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@mcp.tool()
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async def analyze_market_trends(
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address: str,
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radius_meters: int = 1000,
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limit: int = 200
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) -> str:
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"""Analyze market trends and price patterns for an area.
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Args:
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address: The address to analyze trends around
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radius_meters: Search radius in meters (default: 1000)
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limit: Maximum number of deals to analyze (default: 200)
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Returns:
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JSON string containing market trend analysis including:
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- Price trends over time
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- Average prices by property type
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- Market activity levels
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"""
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try:
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# Get deals for the address
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deals = await client.find_recent_deals_for_address(address, radius_meters, limit)
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if not deals:
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return f"No deals found for market analysis near '{address}'"
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# Analyze trends by year
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from collections import defaultdict
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import json
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trends_by_year = defaultdict(list)
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trends_by_type = defaultdict(list)
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for deal in deals:
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deal_date = deal.get("dealDate", "")
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deal_amount = deal.get("dealAmount", 0)
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asset_type = deal.get("assetTypeHeb", "Unknown")
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if deal_date and deal_amount:
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year = deal_date.split('-')[0] if '-' in deal_date else deal_date[:4]
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trends_by_year[year].append(deal_amount)
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trends_by_type[asset_type].append(deal_amount)
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# Calculate yearly trends
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yearly_trends = {}
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for year, prices in trends_by_year.items():
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yearly_trends[year] = {
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"average_price": sum(prices) / len(prices),
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"min_price": min(prices),
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"max_price": max(prices),
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"deal_count": len(prices)
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}
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# Calculate type trends
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type_trends = {}
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for asset_type, prices in trends_by_type.items():
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type_trends[asset_type] = {
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"average_price": sum(prices) / len(prices),
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"min_price": min(prices),
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"max_price": max(prices),
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"deal_count": len(prices)
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}
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return json.dumps({
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"analysis_address": address,
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"analysis_radius_meters": radius_meters,
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"total_deals_analyzed": len(deals),
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"yearly_trends": yearly_trends,
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"property_type_trends": type_trends
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}, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in analyze_market_trends: {e}")
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return f"Error analyzing market trends: {str(e)}"
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@mcp.tool()
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async def compare_neighborhoods(addresses: List[str], radius_meters: int = 500) -> str:
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"""Compare real estate markets between multiple neighborhoods.
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Args:
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addresses: List of addresses to compare (in Hebrew or English)
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radius_meters: Search radius for each address (default: 500)
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Returns:
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JSON string containing comparative analysis of multiple neighborhoods
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"""
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try:
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import json
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comparisons = []
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for address in addresses:
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try:
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deals = await client.find_recent_deals_for_address(address, radius_meters, 100)
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if deals:
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prices = []
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areas = []
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for deal in deals:
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if isinstance(deal, dict):
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if deal.get("dealAmount"):
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prices.append(deal.get("dealAmount", 0))
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if deal.get("assetArea"):
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areas.append(deal.get("assetArea", 0))
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comparison = {
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"address": address,
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"total_deals": len(deals),
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"price_stats": {
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"average_price": sum(prices) / len(prices) if prices else 0,
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"min_price": min(prices) if prices else 0,
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"max_price": max(prices) if prices else 0
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},
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"area_stats": {
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"average_area": sum(areas) / len(areas) if areas else 0,
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"min_area": min(areas) if areas else 0,
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"max_area": max(areas) if areas else 0
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}
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}
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else:
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comparison = {
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"address": address,
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"total_deals": 0,
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"price_stats": {},
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"area_stats": {}
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}
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comparisons.append(comparison)
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except Exception as e:
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logger.error(f"Error comparing {address}: {e}")
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comparisons.append({
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"address": address,
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"error": str(e)
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})
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# Rank neighborhoods by average price
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valid_comparisons = [c for c in comparisons if c.get("price_stats", {}).get("average_price", 0) > 0]
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valid_comparisons.sort(key=lambda x: x["price_stats"]["average_price"], reverse=True)
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return json.dumps({
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"comparison_radius_meters": radius_meters,
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"neighborhoods_compared": len(addresses),
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"ranking_by_average_price": valid_comparisons,
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"all_results": comparisons
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}, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in compare_neighborhoods: {e}")
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return f"Error comparing neighborhoods: {str(e)}"
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# Run the server
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if __name__ == "__main__":
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mcp.run() |