Cloud Run MCP

🚀 MCP Server to Deploy Code to Google Cloud Run

This project enables MCP-compatible AI agents to deploy applications to Google Cloud Run, streamlining the deployment process from various AI - powered environments.

🚀 Quick Start

The following JSON configuration shows how to set up the MCP server for Cloud Run:

"mcpServers":{
  "cloud-run": {
    "command": "npx",
    "args": ["-y", "https://github.com/GoogleCloudPlatform/cloud-run-mcp"]
  }
}

You can deploy code to Cloud Run from multiple sources:

• AI - powered IDEs
• AI assistant apps
• Agent SDKs, such as the Google Gen AI SDK or Agent Development Kit.

⚠️ Important Note

This is the repository of an MCP server to deploy code to Cloud Run. To learn how to host MCP servers on Cloud Run, visit the Cloud Run documentation.

✨ Features

The MCP server provides several useful tools:

• deploy-file-contents: Deploys files to Cloud Run by providing their contents directly.
• list-services: Lists Cloud Run services in a given project and region.
• get-service: Gets details for a specific Cloud Run service.
• deploy-local-files*: Deploys files from the local file system to a Google Cloud Run service.
• deploy-local-folder*: Deploys a local folder to a Google Cloud Run service.
• list-projects*: Lists available GCP projects.
• create-project*: Creates a new GCP project and attaches it to the first available billing account. A project ID can be optionally specified.

* only available when running locally

📦 Installation

💻 Use as local MCP server

Run the Cloud Run MCP server on your local machine using local Google Cloud credentials. This is ideal for users of AI - assisted IDEs (e.g., Cursor) or desktop AI applications (e.g., Claude).

1. Install Node.js (LTS version recommended).
2. Install the Google Cloud SDK and authenticate with your Google account.
3. Log in to your Google Cloud account using the command:

   gcloud auth login
   

4. Set up application credentials using the command:

   gcloud auth application - default login
   

5. Update the MCP configuration file of your MCP client with the following:

      "cloud-run": {
        "command": "npx",
        "args": ["-y", "https://github.com/GoogleCloudPlatform/cloud-run-mcp"]
      }
   

🌐 Use as remote MCP server

⚠️ Important Note

Do not use the remote MCP server without authentication. In the following instructions, we will use IAM authentication to secure the connection to the MCP server from your local machine. This is crucial to prevent unauthorized access to your Google Cloud resources.

Run the Cloud Run MCP server on Cloud Run with connection from your local machine authenticated via IAM. Note that you can only deploy code to the same Google Cloud project as where the MCP server is running.

1. Install the Google Cloud SDK and authenticate with your Google account.
2. Log in to your Google Cloud account using the command:

   gcloud auth login
   

3. Set your Google Cloud project ID using the command:

   gcloud config set project YOUR_PROJECT_ID
   

4. Deploy the Cloud Run MCP server to Cloud Run:

   gcloud run deploy cloud - run - mcp --image us - docker.pkg.dev/cloudrun/container/mcp --no - allow - unauthenticated
   

   When prompted, select a region, for example europe - west1. Note that the MCP server is not publicly accessible and requires authentication via IAM.
5. Run a Cloud Run proxy on your local machine to connect securely using your identity to the remote MCP server running on Cloud Run:

   gcloud run services proxy cloud - run - mcp --port = 3000 --region = REGION --project = PROJECT_ID
   

   This will create a local proxy on port 3000 that forwards requests to the remote MCP server and injects your identity.
6. Update the MCP configuration file of your MCP client with the following:

      "cloud-run": {
        "url": "http://localhost:3000/sse"
      }
   

   If your MCP client does not support the url attribute, you can use [mcp - remote](https://www.npmjs.com/package/mcp - remote):

      "cloud-run": {
        "command": "npx",
        "args": ["-y", "mcp - remote", "http://localhost:3000/sse"]
      }