feat(run): add FastMCP MCP server sample #13425
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Summary of Changes
Hello @jackwotherspoon, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces a new sample application showcasing the deployment of a remote Model Context Protocol (MCP) server built with FastMCP onto Google Cloud Run. The sample includes a basic math server with 'add' and 'subtract' tools, along with all necessary files for containerization, dependency management using uv, and comprehensive documentation covering deployment steps, authentication, and testing. This sample is intended to accompany an upcoming blog post.
Highlights
- New Sample: Added a new sample directory
run/mcp-serverdemonstrating how to deploy a remote MCP server to Google Cloud Run. - FastMCP Server Implementation: Included a simple Python FastMCP server (
server.py) with basicaddandsubtracttools, configured to run using thestreamable-httptransport. - Cloud Run Deployment Setup: Provided a
Dockerfilefor containerizing the Python application and a detailedREADME.mdwith instructions for deploying the server to Cloud Run, including notes on authentication and using thegcloud run services proxy. - Dependency Management: Configured project dependencies using
pyproject.tomlanduv, including auv.lockfile for reproducible builds. - Testing Script: Added a
test_server.pyscript using the FastMCP client to demonstrate how to interact with the deployed remote server.
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Code Review
This pull request introduces a sample FastMCP server designed for deployment on Google Cloud Run. The changes include the server application logic (server.py), a client for testing (test_server.py), Docker configuration (Dockerfile, .dockerignore), project dependencies (pyproject.toml, uv.lock), and comprehensive documentation (README.md).
The overall implementation is clear and demonstrates the setup effectively. Key strengths include the use of uv for dependency management with a lock file for reproducibility, and a well-written README.
My review includes suggestions to ensure runtime correctness related to port handling and to improve build stability by pinning a base image version in the Dockerfile. These suggestions align with common Python best practices (like type safety) and Dockerfile best practices for creating stable and reproducible container images.
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Here is the summary of changes. You are about to add 2 region tags.
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Simple remote MCP server sample deployed to Cloud Run 🚀 ☁️
This accompanies a blog that will go live in the next day or two.