Zillow MCP Server
Provides real-time access to Zillow real estate data, including property search, Zestimates, market trends, and mortgage calculations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Zillow MCP Serversearch for 2-bedroom condos in San Diego under $600k"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Installation
Prerequisites
Python 3.8 or higher
A Zillow Bridge API key (request access at api@bridgeinteractive.com)
Setup
Clone this repository:
git clone https://github.com/yourusername/zillow-mcp-server.git cd zillow-mcp-serverInstall the dependencies:
pip install -r requirements.txtCreate a
.envfile with your Zillow API key:ZILLOW_API_KEY=your_zillow_api_key_here
Running the Server
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 --debugDocker Deployment
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-serverUsage with Claude Desktop
Add the Zillow MCP server to your Claude Desktop configuration file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%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?Available Resources
Property Resource
Get property information as a formatted text resource:
zillow://property/{property_id}Market Trends Resource
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 Architecture
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 format may change; refer to Zillow's documentation for updates
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Fork the repository
Create a feature branch:
git checkout -b feature-nameCommit your changes:
git commit -m 'Add some feature'Push to the branch:
git push origin feature-nameSubmit a pull request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
FastMCP for the Pythonic MCP implementation
Zillow for providing real estate data APIs
Model Context Protocol for the standard protocol specification
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