churn-mcp-server

Exposes the existing ml-production-template churn-prediction API as an MCP tool, so an MCP-aware client (Claude Desktop, Claude Code, or any MCP client) can call predict_churn directly.

Design

This is a thin adapter, not a rewrite of the model server. It does not load the model itself -- it calls the already-deployed churn-api service's /predict endpoint over HTTP. That means:

• The MCP layer can be redeployed or scaled independently of the model.

• If churn-api restarts, this doesn't need to.

• The "real" ML system (FastAPI + MLflow) is untouched; MCP is an additive interface on top of it, matching how you'd actually integrate MCP into an existing production service rather than baking it into the model server.

Two tools are exposed:

• predict_churn -- the 19-field customer schema from the real API, returns churn_probability / churn_prediction / threshold_used.

• check_model_health -- proxies the API's /health endpoint.

Related MCP server: MCP from API

A version note

The official mcp Python SDK shipped a v2.0 recently that renamed FastMCP to MCPServer and moved import paths. This project pins mcp<2.0.0 and uses from mcp.server.fastmcp import FastMCP, since that's still the stable, widely-documented line as of this writing. If you're reading this later and pip install mcp pulls v2 by default, you'll need to port the import (mcp.server.fastmcp.FastMCP → mcp.server.MCPServer) — check the SDK's migration guide before assuming the code below still applies as-is.

Local test (stdio, no Docker)

pip install -r requirements.txt
$env:MCP_TRANSPORT="stdio"
$env:CHURN_API_URL="http://localhost:8000"   # your local churn-api
python server.py

Point Claude Desktop's claude_desktop_config.json at it:

{
  "mcpServers": {
    "churn-predictor": {
      "command": "python",
      "args": ["C:\\path\\to\\churn-mcp-server\\server.py"],
      "env": { "CHURN_API_URL": "http://localhost:8000" }
    }
  }
}

Deploying into the existing kind cluster

This assumes the churn-api Deployment + Service from the Kubernetes project are already running in your kind cluster.

docker build -t churn-mcp-server:local .
kind load docker-image churn-mcp-server:local --name <your-cluster-name>
kubectl apply -f k8s/deployment.yaml
kubectl get pods -l app=churn-mcp-server

Verify:

kubectl port-forward svc/churn-mcp-server 8080:80
curl http://localhost:8080/healthz