awslabs-eks-mcp-server

import uvicorn from mcp_server.server import ModelContextProtocolServer from mcp_server.provider import ModelProvider class MyModelProvider(ModelProvider): def __init__(self): super().__init__('my-model-provider-id') async def get_model_artifact_uri(self, model_id: str) -> str: # In a real scenario, this would return an S3 URI or similar print(f"Request for model_id: {model_id}") return f"s3://my-bucket/models/{model_id}/artifact.tar.gz" async def get_model_context(self, model_id: str) -> dict: # Return context specific to the model, e.g., framework, version return {"framework": "pytorch", "version": "1.13.1", "device": "cuda"} # Instantiate your model provider provider = MyModelProvider() # Create the MCP server instance app = ModelContextProtocolServer(provider=provider, port=8000) # Run the FastAPI app using Uvicorn # In a real deployment, you'd use a proper process manager (e.g., systemd, Kubernetes deployment) # For local testing, you can run this file directly and access http://localhost:8000/docs # Example of running the app via uvicorn programmatically if __name__ == "__main__": print("Starting MCP server on http://localhost:8000") # Use reload=True for development, remove in production uvicorn.run(app.get_app(), host="0.0.0.0", port=8000)