Nitzan Pomerantz
353 lines
13 KiB
Python
353 lines
13 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 List, Dict
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from mcp.server.fastmcp import FastMCP
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from .govmap import GovmapClient
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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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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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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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def get_deals_by_radius(latitude: float, longitude: float, radius_meters: int = 500) -> 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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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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# Note: GovmapClient expects (longitude, latitude) tuple
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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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return json.dumps({
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"total_deals": len(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": 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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def get_street_deals(polygon_id: str, limit: int = 100) -> 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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Returns:
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JSON string containing recent real estate deals for the street
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"""
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try:
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deals = client.get_street_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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return json.dumps({
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"total_deals": len(deals),
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"polygon_id": polygon_id,
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"deals": 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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def find_recent_deals_for_address(address: str, years_back: int = 2) -> 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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Returns:
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JSON string containing recent real estate deals for the address
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"""
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try:
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deals = client.find_recent_deals_for_address(address, years_back)
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if not deals:
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return f"No deals found for address '{address}'"
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# Calculate basic 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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return json.dumps({
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"search_address": address,
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"years_back": years_back,
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"total_deals": len(deals),
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"market_statistics": stats,
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"deals": 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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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 = 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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return json.dumps({
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"total_deals": len(deals),
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"polygon_id": polygon_id,
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"deals": 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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def analyze_market_trends(address: str, years_back: int = 3) -> 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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years_back: How many years of data to analyze (default: 3)
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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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- Price per square meter trends
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"""
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try:
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# Get deals for the address
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deals = client.find_recent_deals_for_address(address, years_back)
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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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yearly_data = defaultdict(list)
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property_types: Dict[str, int] = defaultdict(int)
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neighborhoods = set()
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for deal in deals:
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date_str = deal.get('dealDate', '')
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if date_str:
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year = date_str[:4]
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price = deal.get('dealAmount')
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area = deal.get('assetArea')
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prop_type = deal.get('assetTypeHeb', deal.get('propertyTypeDescription', 'Unknown'))
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neighborhood = deal.get('settlementNameHeb', deal.get('neighborhood'))
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if neighborhood:
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neighborhoods.add(neighborhood)
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if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
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yearly_data[year].append({
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'price': price,
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'area': area,
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'price_per_sqm': price / area
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})
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property_types[prop_type] += 1
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# Calculate yearly trends
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yearly_trends = {}
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for year, year_deals in yearly_data.items():
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if year_deals:
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yearly_trends[year] = {
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"average_price": sum(d['price'] for d in year_deals) / len(year_deals),
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"min_price": min(d['price'] for d in year_deals),
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"max_price": max(d['price'] for d in year_deals),
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"average_area": sum(d['area'] for d in year_deals) / len(year_deals),
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"average_price_per_sqm": sum(d['price_per_sqm'] for d in year_deals) / len(year_deals),
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"deal_count": len(year_deals)
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}
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# Calculate price trend direction
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price_trend_analysis = {}
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years_sorted = sorted(yearly_trends.keys())
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if len(years_sorted) >= 2:
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first_year_avg = yearly_trends[years_sorted[0]]['average_price_per_sqm']
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last_year_avg = yearly_trends[years_sorted[-1]]['average_price_per_sqm']
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trend_percentage = ((last_year_avg - first_year_avg) / first_year_avg) * 100
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trend_direction = "rising" if trend_percentage > 5 else "declining" if trend_percentage < -5 else "stable"
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price_trend_analysis = {
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"trend_direction": trend_direction,
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"trend_percentage": round(trend_percentage, 1),
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"first_year": years_sorted[0],
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"last_year": years_sorted[-1],
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"first_year_avg_price_per_sqm": round(first_year_avg, 0),
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"last_year_avg_price_per_sqm": round(last_year_avg, 0)
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}
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return json.dumps({
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"analysis_address": address,
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"analysis_period_years": years_back,
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"total_deals_analyzed": len(deals),
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"neighborhoods": list(neighborhoods),
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"property_types": dict(property_types),
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"yearly_trends": yearly_trends,
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"price_trend_analysis": price_trend_analysis
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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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def compare_addresses(addresses: List[str]) -> str:
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"""Compare real estate markets between multiple addresses.
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Args:
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addresses: List of addresses to compare (in Hebrew or English)
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Returns:
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JSON string containing comparative analysis of multiple addresses
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"""
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try:
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comparisons = []
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for address in addresses:
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try:
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deals = client.find_recent_deals_for_address(address, 2)
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if deals:
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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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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 addresses 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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"addresses_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_addresses: {e}")
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return f"Error comparing addresses: {str(e)}"
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# Run the server
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if __name__ == "__main__":
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mcp.run() |