Google Cloud Run MCP Server

The Cloud Run MCP server turns Google Cloud Run into a first‑class target for AI‑powered deployment workflows. By exposing a set of tools and prompts over the Model Context Protocol, it allows agents—whether built into an IDE, a command‑line interface, or a custom application—to treat Cloud Run as a deployable artifact. This solves the problem of manually managing cloud infrastructure while still leveraging AI assistants to orchestrate code releases, monitor services, and troubleshoot errors.

At its core, the server provides a suite of deployment tools that accept either file contents or entire local folders. A single call can push a code snippet directly to Cloud Run, while streams an entire project directory. The server also offers introspection tools such as , , and so an agent can query the state of deployed services, fetch logs, or inspect error messages—all within a single conversational context. These capabilities make it trivial for an AI assistant to move from “what is the current deployment state?” to “deploy this new version” in a few conversational turns.

Developers benefit from the server’s tight integration with popular AI workflows. In Gemini CLI, the Cloud Run MCP is installed as an extension and can be invoked with natural language commands like or , which are mapped to pre‑filled tool calls. IDEs such as VSCode, Cursor, or Claude Desktop can be configured to connect to the same MCP server, enabling a unified deployment experience across tools. Because the server runs locally with user credentials, developers retain full control over project selection and billing while still enjoying AI‑driven automation.

Real‑world scenarios include continuous integration pipelines where an AI agent reviews pull requests, runs tests, and automatically deploys passing changes to Cloud Run. It also fits into DevOps workflows where a chatbot can answer “Where is my latest service running?” and immediately provide the log tail. For teams that use Google’s AI tools, the server offers a seamless bridge between model function calls and cloud operations, reducing context switches and speeding up release cycles.

What sets this MCP server apart is its native support for Cloud Run’s deployment model and the inclusion of both file‑level and folder‑level tools. Unlike generic container registries, it handles the entire lifecycle—building, deploying, and monitoring—within a single protocol interface. This unified approach gives AI assistants the power to orchestrate end‑to‑end cloud deployments without exposing developers to low‑level APIs or command‑line intricacies.