by googlecloudplatform
Deploy and manage MCP-compatible AI apps on Google Cloud Run — automate Cloud Run deployments, list services, and manage
Connects AI agents directly to Google Cloud Run for deploying applications, listing services, and managing projects. Makes cloud deployment as simple as talking to your AI assistant.
Cloud Run MCP Server is an official MCP server published by googlecloudplatform that provides AI assistants with tools and capabilities via the Model Context Protocol. Deploy and manage MCP-compatible AI apps on Google Cloud Run — automate Cloud Run deployments, list services, and manage It is categorized under cloud infrastructure, developer tools.
You can install Cloud Run MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
Apache-2.0
Cloud Run MCP Server is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Cloud Run MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Cloud Run MCP Server reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
I recommend Cloud Run MCP Server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We wired Cloud Run MCP Server into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: Cloud Run MCP Server is the kind of server we cite when onboarding engineers to host + tool permissions.
Cloud Run MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired Cloud Run MCP Server into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
We evaluated Cloud Run MCP Server against two servers with overlapping tools; this profile had the clearer scope statement.
Cloud Run MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Cloud Run MCP Server surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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Enable MCP-compatible AI agents to deploy apps to Cloud Run.
"mcpServers":{
"cloud-run": {
"command": "npx",
"args": ["-y", "@google-cloud/cloud-run-mcp"]
}
}
Deploy from Gemini CLI and other AI-powered CLI agents:
<img src="https://raw.githubusercontent.com/GoogleCloudPlatform/cloud-run-mcp/refs/heads/main/.github/images/deploycli.gif" width="800">Deploy from AI-powered IDEs:
<img src="https://raw.githubusercontent.com/GoogleCloudPlatform/cloud-run-mcp/refs/heads/main/.github/images/deploy_from_ide.gif" width="800">Deploy from AI assistant apps:
<img src="https://raw.githubusercontent.com/GoogleCloudPlatform/cloud-run-mcp/refs/heads/main/.github/images/deploy_from_apps.gif" width="800">Deploy from agent SDKs, like the Google Gen AI SDK or Agent Development Kit.
[!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.
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.
get-service-log: Gets Logs and Error Messages for a specific 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 attach it to the first available billing account. A project ID can be optionally specified.
* only available when running locally
Prompts are natural language commands that can be used to perform common tasks. They are shortcuts for executing tool calls with pre-filled arguments.
deploy: Deploys the current working directory to Cloud Run. If a service name is not provided, it will use the DEFAULT_SERVICE_NAME environment variable, or the name of the current working directory.logs: Gets the logs for a Cloud Run service. If a service name is not provided, it will use the DEFAULT_SERVICE_NAME environment variable, or the name of the current working directory.The Cloud Run MCP server can be configured using the following environment variables:
| Variable | Description |
|---|---|
GOOGLE_CLOUD_PROJECT | The default project ID to use for Cloud Run services. |
GOOGLE_CLOUD_REGION | The default region to use for Cloud Run services. |
DEFAULT_SERVICE_NAME | The default service name to use for Cloud Run services. |
SKIP_IAM_CHECK | Controls whether to check for IAM permissions for a Cloud Run service. Set to false to enable checks. This is true by default which is a recommended way to make the service public. |
ENABLE_HOST_VALIDATION | Prevents DNS Rebinding attacks by validating the Host header. This is disabled by default. |
ALLOWED_HOSTS | Comma-separated list of allowed Host headers (if host validation is enabled). The default value is localhost,127.0.0.1,::1. |
To install this as a Gemini CLI extension, run the following command:
Install the extension:
gemini extensions install https://github.com/GoogleCloudPlatform/cloud-run-mcp
Log in to your Google Cloud account using the command:
gcloud auth login
Set up application credentials using the command:
gcloud auth application-default login
Most MCP clients require a configuration file to be created or modified to add the MCP server.
The configuration file syntax can be different across clients. Please refer to the following links for the latest expected syntax:
Once you have identified how to configure your MCP client, select one of these two options to set up the MCP server. We recommend setting up as a local MCP server using Node.js.
Run the Cloud Run MCP server on your local machine using local Google Cloud credentials. This is best if you are using an AI-assisted IDE (e.g. Cursor) or a desktop AI application (e.g. Claude).
Install the Google Cloud SDK and authenticate with your Google account.
Log in to your Google Cloud account using the command:
gcloud auth login
Set up application credentials using the command:
gcloud auth application-default login
Then configure the MCP server using either Node.js or Docker:
Install Node.js (LTS version recommended).
Update the MCP configuration file of your MCP client with the following:
"cloud-run": {
"command": "npx",
"args": ["-y", "@google-cloud/cloud-run-mcp"]
}
[Optional] Add default configurations
"cloud-run": {
"command": "npx",
"args": ["-y", "@google-cloud/cloud-run-mcp"],
"env": {
"GOOGLE_CLOUD_PROJECT": "PROJECT_NAME",
"GOOGLE_CLOUD_REGION": "PROJECT_REGION",
"DEFAULT_SERVICE_NAME": "SERVICE_NAME"
}
}
See Docker's MCP catalog, or use these manual instructions:
Install Docker
Update the MCP configuration file of your MCP client with the following:
"cloud-run": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"GOOGLE_APPLICATION_CREDENTIALS",
"-v",
"/local-directory:/local-directory",
"mcp/cloud-run-mcp:latest"
],
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": "/Users/slim/.config/gcloud/application_default-credentials.json",
"DEFAULT_SERVICE_NAME": "SERVICE_NAME"
}
}
[!WARNING]
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 important to prevent unauthorized access to your Google Cloud resources.
Run the Cloud Run MCP server itself on Cloud Run with connection from your local machine authenticated via IAM. With this option, you will only be able to deploy code to the same Google Cloud project as where the MCP server is running.
Install the Google Cloud SDK and authenticate with your Google account.
Log in to your Google Cloud account using the command:
gcloud auth login
Set your Google Cloud project ID using the command:
gcloud config set project YOUR_PROJECT_ID
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, pick a region, for example europe-west1.
Note that the MCP server is not publicly accessible, it requires authentication via IAM.
[Optional] Add default configurations
gcloud run services update cloud-run-mcp --region=REGION --update-env-vars GOOGLE_CLOUD_PROJECT=PROJECT_NAME,GOOGLE_CLOUD_REGION=PROJECT_REGION,DEFAULT_SERVICE_NAME=SERVICE_NAME,SKIP_IAM_CHECK=false
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.
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:
"cloud-run": {
"command": "npx",
"args": ["-y", "mcp-remote", "http://localhost:3000/sse"]
}
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.