What is Zillow Mcp Server

Zillow MCP Server is a Model Context Protocol server that provides real-time access to Zillow real estate data, developed using Python and FastMCP.

Use cases

Use cases include building real estate listing platforms, integrating Zillow data into mobile apps, conducting market research, and providing mortgage calculation tools.

How to use

To use Zillow MCP Server, clone the repository, install dependencies, set up your Zillow API key in a .env file, and run the server in your preferred mode (standard, HTTP, or debug). It can also be run using Docker.

Key features

Key features include property search by location and price, detailed property information, access to Zillow’s home valuation data (Zestimates), market trend analysis, a mortgage calculator, and API health checks.

Where to use

Zillow MCP Server can be utilized in real estate applications, market analysis tools, property management systems, and any application requiring real estate data.

README.md - Zillow MCP Server

A Model Context Protocol (MCP) server that provides real-time access to Zillow real estate data, built with Python and FastMCP.

Features

• 🏠 Property Search: Search for properties by location, price range, and property features
• 💰 Property Details: Get detailed information about specific properties
• 📊 Zestimates: Access Zillow’s proprietary home valuation data
• 📈 Market Trends: View real estate market trends for any location
• 🧮 Mortgage Calculator: Calculate mortgage payments based on various inputs
• 🔍 Health Check: Verify API connectivity and monitor performance

Requirements

• Python 3.8 or higher
• A Zillow Bridge API key (request access at [email protected])

Installation

1. Clone this repository:

git clone https://github.com/rohitsingh-iitd/zillow-mcp-server
cd zillow-mcp-server

2. Install the dependencies:

pip install -r requirements.txt

3. Create a .env file with your Zillow API key:

ZILLOW_API_KEY=your_zillow_api_key_here

Usage

Run the server with options:

# Standard stdio mode (for Claude Desktop)
python zillow_mcp_server.py

# HTTP server mode (for remote access)
python zillow_mcp_server.py --http --port 8000

# Debug mode for more verbose logging
python zillow_mcp_server.py --debug

You can also run the server using Docker:

# Build the Docker image
docker build -t zillow-mcp-server .

# Run with environment variables
docker run -p 8000:8000 -e ZILLOW_API_KEY=your_key_here zillow-mcp-server

# Or using an env file
docker run -p 8000:8000 --env-file .env zillow-mcp-server

Usage with Claude Desktop

Add the Zillow MCP server to your Claude Desktop configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "zillow": {
      "command": "python",
      "args": [
        "/path/to/zillow_mcp_server.py"
      ]
    }
  }
}

For remote HTTP server:

{
  "mcpServers": {
    "zillow-remote": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://your-mcp-server-url.com/sse"
      ]
    }
  }
}

Available Tools

search_properties

Search for properties based on various criteria:

search_properties(
    location: str,
    type: str = "forSale",
    min_price: Optional[int] = None,
    max_price: Optional[int] = None,
    beds_min: Optional[int] = None,
    beds_max: Optional[int] = None,
    baths_min: Optional[float] = None,
    baths_max: Optional[float] = None,
    home_types: Optional[List[str]] = None
)

Example usage in Claude:

Please search for properties in Seattle with prices between $500,000 and $800,000.

get_property_details

Get detailed information about a specific property:

get_property_details(
    property_id: str = None,
    address: str = None
)

Example usage in Claude:

Can you get the details for the property with ID 12345?

get_zestimate

Get Zillow’s estimated value for a property:

get_zestimate(
    property_id: str = None,
    address: str = None
)

Example usage in Claude:

What's the Zestimate for 123 Main St, Seattle, WA?

get_market_trends

Get real estate market trends for a specific location:

get_market_trends(
    location: str,
    metrics: List[str] = ["median_list_price", "median_sale_price", "median_days_on_market"],
    time_period: str = "1year"
)

Example usage in Claude:

What are the current real estate trends in Boston over the past year?

calculate_mortgage

Calculate mortgage payments and related costs:

calculate_mortgage(
    home_price: int,
    down_payment: int = None,
    down_payment_percent: float = None,
    loan_term: int = 30,
    interest_rate: float = 6.5,
    annual_property_tax: int = None,
    annual_homeowners_insurance: int = None,
    monthly_hoa: int = 0,
    include_pmi: bool = True
)

Example usage in Claude:

Calculate the monthly mortgage payment for a $600,000 house with 20% down and a 6% interest rate.

check_health

Verify the Zillow API connection and get server status:

check_health()

Example usage in Claude:

Please check if the Zillow API is currently responsive.

get_server_tools

Get a list of all available tools on this server:

get_server_tools()

Example usage in Claude:

What tools are available in the Zillow MCP server?

Resources

Get property information as a formatted text resource:

zillow://property/{property_id}

Get market trends information as a formatted text resource:

zillow://market-trends/{location}

Error Handling

The server implements robust error handling with:

• Automatic retries with exponential backoff
• Detailed error logging
• Rate limit handling
• Connection timeouts
• Graceful degradation

Technical Details

This MCP server is built using:

• FastMCP: A Pythonic framework for building Model Context Protocol servers
• Requests: For making HTTP requests to the Zillow Bridge API with connection pooling and retries
• Backoff: For implementing exponential backoff retry logic
• python-dotenv: For managing environment variables

The server provides both tools (interactive functions) and resources (static data) that Claude can access to provide real estate information to users.

Limitations and Considerations

• Zillow’s API has usage limits (typically 1,000 requests per day per dataset)
• Zillow’s terms of service prohibit storing data locally; all requests must be dynamic
• You must properly attribute data to Zillow in the user interface
• The Bridge API functionality may change over time, requiring server updates

License

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

Acknowledgments

• Zillow for providing the Bridge API
• Anthropic for the Model Context Protocol specification