This example demonstrates how to enable MCP (Model Context Protocol) support in Feast, allowing AI agents and applications to interact with your features through standardized MCP interfaces.

1. Python 3.8+
2. Feast installed
3. FastAPI MCP library

1. Install Feast with MCP support:

pip install feast[mcp]

Alternatively, you can install the dependencies separately:

pip install feast
pip install fastapi_mcp

1. Navigate to this example directory within your cloned Feast repository:

cd examples/mcp_feature_store

2. Initialize a Feast repository in this directory. We'll use the existing feature_store.yaml that's already configured for MCP:

feast init . 

This will create a data subdirectory and a feature_repo subdirectory if they don't exist, and will use the feature_store.yaml present in the current directory (examples/mcp_feature_store).

3. Apply the feature store configuration:

cd feature_repo 
feast apply
cd .. # Go back to examples/mcp_feature_store for the next steps

Start the Feast feature server with MCP support:

feast serve --host 0.0.0.0 --port 6566

If MCP is properly configured, you should see a log message indicating that MCP support has been enabled:

INFO:feast.feature_server:MCP support has been enabled for the Feast feature server

The fastapi_mcp integration automatically exposes your Feast feature server's FastAPI endpoints as MCP tools. This means AI assistants can:

• Call /get-online-features to retrieve features from the feature store
• Use /health to check server status

The key configuration that enables MCP support:

feature_server:
    type: mcp                    # Use MCP feature server type
    enabled: true               # Enable feature server
    mcp_enabled: true           # Enable MCP protocol support
    mcp_server_name: "feast-feature-store"
    mcp_server_version: "1.0.0"