nadlan-mcp/README.md

Israel Real Estate MCP

A Python-based Mission Control Program (MCP) for interacting with the Israeli government's public real estate data API (Govmap). This tool allows real estate agents and professionals to query recent property deals based on addresses, search for properties, and retrieve detailed market information.

Description

This project provides a comprehensive Python interface to the Israeli government's Govmap API, enabling users to:

• Search for property addresses using autocomplete
• Find geographical coordinates for addresses
• Retrieve block (Gush) and parcel (Helka) information
• Query recent real estate deals by location
• Get detailed street and neighborhood deal information
• Analyze market trends in specific areas

Features

• Address Autocomplete: Search for addresses using free text and get precise coordinates
• Geospatial Data: Retrieve block and parcel information for any coordinate point
• Deal Discovery: Find real estate deals within a specified radius of any location
• Street Analysis: Get detailed deal information for specific streets
• Neighborhood Insights: Analyze deals within entire neighborhoods
• Time-based Filtering: Search for deals within specific date ranges
• Comprehensive Search: High-level function that combines all features to find deals for any address
• Error Handling: Robust error handling for network issues and API failures
• Logging: Detailed logging for debugging and monitoring

Installation

Prerequisites

• Python 3.7 or higher
• pip package manager

Setup Instructions

1. Clone the repository:

   git clone <repository-url>
   cd nadlan-mcp
   

2. Create a virtual environment:

   python -m venv venv
   

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate
     

   • On macOS/Linux:

     source venv/bin/activate
     

4. Install required packages:

   pip install -r requirements.txt
   

Usage

Basic Usage

from nadlan_mcp import GovmapClient

# Initialize the client
client = GovmapClient()

# Search for recent deals for a specific address
address = "סוקולוב 38 חולון"
deals = client.find_recent_deals_for_address(address, years_back=2)

print(f"Found {len(deals)} deals for {address}")
for deal in deals[:5]:  # Show first 5 deals
    print(f"Address: {deal.get('address')}")
    print(f"Date: {deal.get('dealDate')}")
    print(f"Price: {deal.get('price')}")
    print("---")

Advanced Usage Examples

1. Address Autocomplete

from nadlan_mcp import GovmapClient

client = GovmapClient()

# Search for an address
result = client.autocomplete_address("בן יהודה 1 תל אביב")
if result['results']:
    best_match = result['results'][0]
    print(f"Found: {best_match.get('displayName')}")
    print(f"Coordinates: {best_match.get('point')}")

2. Get Block and Parcel Information

# Get coordinates from address first
autocomplete_result = client.autocomplete_address("דיזנגוף 50 תל אביב")
point = tuple(autocomplete_result['results'][0]['point'])

# Get block and parcel info
gush_helka = client.get_gush_helka(point)
print(f"Block/Parcel info: {gush_helka}")

3. Find Deals by Radius

# Find deals within 100 meters of a point
point = (3870923.95, 3766288.07)  # Example coordinates
deals = client.get_deals_by_radius(point, radius=100)

print(f"Found {len(deals)} deals within 100m")
for deal in deals:
    print(f"- {deal.get('address')}: {deal.get('price')}")

4. Street-specific Deals

# Get detailed street deals for a specific polygon
polygon_id = "52190246"
street_deals = client.get_street_deals(
    polygon_id, 
    limit=10, 
    start_date="2023-01", 
    end_date="2024-01"
)

print(f"Found {len(street_deals)} street deals")

5. Neighborhood Analysis

# Get neighborhood deals
neighborhood_deals = client.get_neighborhood_deals(
    polygon_id="52282030",
    limit=20,
    start_date="2023-01",
    end_date="2024-01"
)

print(f"Found {len(neighborhood_deals)} neighborhood deals")

Running the Example Script

The project includes a main example script that demonstrates basic usage:

python -m nadlan_mcp.main

Or run it directly:

python nadlan_mcp/main.py

API Reference

GovmapClient Class

The main class for interacting with the Govmap API.

Methods

__init__(base_url: str = "https://www.govmap.gov.il/api/")

Initialize the client with the base API URL.

autocomplete_address(search_text: str) -> Dict[str, Any]

Search for addresses using autocomplete functionality.

• Parameters:
  • search_text: The address to search for (Hebrew or English)
• Returns: Dictionary with search results and coordinates
• Raises: requests.RequestException, ValueError

get_gush_helka(point: Tuple[float, float]) -> Dict[str, Any]

Get block (Gush) and parcel (Helka) information for coordinates.

• Parameters:
  • point: Tuple of (longitude, latitude)
• Returns: Dictionary with block and parcel data
• Raises: requests.RequestException, ValueError

get_deals_by_radius(point: Tuple[float, float], radius: int = 50) -> List[Dict[str, Any]]

Find deals within a specified radius of a point.

• Parameters:
  • point: Tuple of (longitude, latitude)
  • radius: Search radius in meters (default: 50)
• Returns: List of deal dictionaries
• Raises: requests.RequestException

get_street_deals(polygon_id: str, limit: int = 10, start_date: Optional[str] = None, end_date: Optional[str] = None) -> List[Dict[str, Any]]

Get detailed deals for a specific street.

• Parameters:
  • polygon_id: The polygon ID for the street
  • limit: Maximum number of deals to return (default: 10)
  • start_date: Start date in 'YYYY-MM' format
  • end_date: End date in 'YYYY-MM' format
• Returns: List of detailed deal information
• Raises: requests.RequestException

get_neighborhood_deals(polygon_id: str, limit: int = 10, start_date: Optional[str] = None, end_date: Optional[str] = None) -> List[Dict[str, Any]]

Get deals within the same neighborhood.

• Parameters:
  • polygon_id: The polygon ID for the area
  • limit: Maximum number of deals to return (default: 10)
  • start_date: Start date in 'YYYY-MM' format
  • end_date: End date in 'YYYY-MM' format
• Returns: List of neighborhood deals
• Raises: requests.RequestException

find_recent_deals_for_address(address: str, years_back: int = 2) -> List[Dict[str, Any]]

Main function: Find all relevant deals for an address (combines all other methods).

• Parameters:
  • address: The address to search for
  • years_back: How many years back to search (default: 2)
• Returns: List of all relevant deals, sorted by date
• Raises: ValueError, requests.RequestException

Govmap API Endpoints

This project uses the following Govmap API endpoints:

• Autocomplete: POST /search-service/autocomplete
• Entities by Point: POST /layers-catalog/entitiesByPoint
• Deals by Radius: GET /real-estate/deals/{point}/{radius}
• Street Deals: GET /real-estate/street-deals/{polygon_id}
• Neighborhood Deals: GET /real-estate/neighborhood-deals/{polygon_id}

API Base URL

https://www.govmap.gov.il/api/

Error Handling

The client includes comprehensive error handling:

• Network Errors: Handles connection issues and API timeouts
• API Errors: Manages HTTP error responses and invalid data
• Data Validation: Validates API responses and handles missing data
• Logging: Provides detailed logging for debugging issues

Logging

The project uses Python's built-in logging module. To enable debug logging:

import logging
logging.basicConfig(level=logging.DEBUG)

Dependencies

• requests: HTTP library for API calls
• python-dotenv: Environment variable management (for future configuration)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Disclaimer

This tool is for educational and professional use only. Please respect the Govmap API terms of service and rate limits. The authors are not responsible for any misuse of this tool.

Support

For issues, questions, or contributions, please create an issue in the repository.

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Note: This project is not officially affiliated with the Israeli government or Govmap. It is an independent tool created to facilitate access to public real estate data.