RentCast MCP Server

How to use

You will configure an MCP client to connect to the RentCast MCP Server and then issue queries that map to RentCast data endpoints. Use the available tools to fetch detailed property data, valuations, rent estimates, and market statistics for ZIP code areas. You can also retrieve active property listings in a ZIP code and review market trends over time.

Key tools you can use:

• get_property_data for a specific property ID to retrieve detailed information
• get_property_valuation to obtain property value estimates
• get_rent_estimate to fetch rent estimates
• get_market_statistics to obtain market data for a ZIP code area
• get_property_listings to list active properties in a ZIP code area

Common usage patterns include querying market statistics for a ZIP code, listing properties in a ZIP code, and fetching valuations or property details for property IDs you are researching. When you interact with Claude or your MCP client, you will call these tools with the appropriate parameters provided by your workflow.

How to install

# Prerequisites
- Python 3.12 or higher
- MCP Python SDK
- httpx
- python-dotenv

# Optional tools for local development
- uv (recommended) to manage environments and run MCP locally

Install the runtime and dependencies as described below to set up the RentCast MCP Server locally. Follow these steps in order to have a working server you can connect to from an MCP client.

Step 1: Install uv (recommended) to manage the local runtime and virtual environments.

curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Clone the RentCast MCP Server repository and enter the project directory.

git clone https://github.com/yourusername/rentcast-mcp-server.git
cd rentcast-mcp-server

Step 3: Create and activate a virtual environment.

# Create virtual environment
uv venv

# Activate virtual environment
# macOS/Linux:
source .venv/bin/activate
# Windows:
.venv\Scripts\activate

Step 4: Install dependencies.

# Option 1: Using uv (recommended)
uv sync

# Option 2: Using pip with requirements.txt
pip install -r requirements.txt

# Option 3: Install as editable package
uv pip install -e .

Step 5: Set up environment variables. Create a .env file in the project root with your RentCast API key.

RENTCAST_API_KEY=your_api_key_here

Additional install steps

If you plan to use the Claude Desktop integration, install the MCP CLI globally and configure Claude to run the RentCast MCP Server from your system path.

uv tool install "mcp[cli]"

Notes on configuration (example for Claude Desktop)

{
  "mcpServers": {
    "RentCast": {
      "command": "/Users/<USERNAME>/.local/share/uv/tools/mcp/bin/mcp",
      "args": ["run", "/full/path/to/rentcast-mcp-server/src/rentcast_mcp_server/server.py"]
    }
  }
}

Running the server locally

You can start the server in development or testing environments using the provided commands.

rentcast-mcp
```

```
uv run python src/rentcast_mcp_server/server.py

Starting via MCP CLI

If you prefer using the MCP CLI to run the server, you can execute the following:

mcp run src/rentcast_mcp_server/server.py

get_property_data

Fetches detailed data for a specific property by ID, returning attributes such as address, size, features, and recent updates.

get_property_valuation

Returns property value estimates based on RentCast data and market factors.

get_rent_estimate

Provides rent estimates for a property, useful for investment analysis and pricing.

get_market_statistics

Retrieves market statistics for a ZIP code area, including trends, averages, and medians.

get_property_listings

Lists active property listings within a ZIP code area.