Google Cloud Run MCP server
Create a powerful Model Context Protocol (MCP) server for Google Cloud Run to deploy, manage, and scale containerized applications without managing servers. This integration enables AI agents to automate service deployment, manage revisions, split traffic for gradual rollouts, and monitor serverless applications with secure service account authentication.
Setting up an MCP server
This article covers the standard steps for creating an MCP server in AI Gateway and connecting it to an AI client. The steps are the same for every integration — application-specific details (API credentials, OAuth endpoints, and scopes) are covered in the Authentication section on this page.
Before you begin
You'll need:
- Access to AI Gateway with permission to create MCP servers.
- API credentials for the application you're connecting (see the Authentication section on this page for what to collect).
Create an MCP server
Find the app in the catalog
- Sign in to AI Gateway and select App Catalog from the left navigation.
- Search for the application you want to connect, then select it from the catalog.
- Select Create MCP Server to start the wizard.
App Configuration
Confirm the Base URL for the API, then, under Tools, select the endpoints you want to expose. Select Next.
MCP Server Setup
- Enter a Name for your server — something descriptive that identifies both the application and its purpose.
- Enter a Description so your team knows what the server is for.
- Set the log level: choose Production Mode for terser logs, or Non-Production Mode for more verbose logs that can help with debugging.
- Select Next.
Authentication
Enter the authentication details for the application. This varies by service — see the Authentication section on this page for the specific credentials, OAuth URLs, and scopes to use.
Review
Look over the summary of your MCP server configuration, then select Create & Deploy. AI Gateway provisions the server and provides a server URL you'll use when configuring your AI client.
Connect to an AI client
Once your server is deployed, you'll need to add it to the AI client your team uses. Select your client for setup instructions:
Tips
- You can create multiple MCP servers for the same application — for example, a read-only server for reporting agents and a read-write server for automation workflows.
- If you're unsure which OAuth scopes to request, start with the minimum read-only set and add write scopes only when needed. Most application pages include scope recommendations.
Authentication
Google Cloud Run uses OAuth 2.0 with service accounts for API access. Create a service account in your Google Cloud project with the Cloud Run Admin role and download a JSON key file. In Google Cloud Console, go to IAM & Admin > Service Accounts, create a service account with the Cloud Run Admin role, and download a JSON key.
| Value | Setting |
|---|---|
| Token endpoint | https://oauth2.googleapis.com/token |
| Scopes | https://www.googleapis.com/auth/cloud-platform |
Available tools
These tools enable AI agents to manage the full Cloud Run lifecycle—deployment, configuration, traffic management, and monitoring. Together they support CI/CD automation, canary deployments, and serverless infrastructure as code.
| Tool | Description |
|---|---|
| List services | Find all Cloud Run services in a region or project |
| Create service | Deploy a container image as a new Cloud Run service |
| Get service | Retrieve details about a service (memory, CPU, environment variables) |
| Update service | Change service configuration (memory, concurrency, image) |
| Delete service | Remove a service and stop all revisions |
| List revisions | View all versions of a service |
| Get revision | Fetch details about a specific revision |
| Delete revision | Remove an old revision to save costs |
| Update traffic | Split traffic between revisions (for canary or blue-green deployments) |
| Create domain mapping | Link a custom domain to a service |
| Delete domain mapping | Remove a custom domain |
| List jobs | View Cloud Run jobs (batch tasks) |
| Create job | Create a new job for scheduled or event-driven work |
| Run job | Execute a job immediately |
| List executions | View past job executions and their status |
Tips
Build workflows that deploy from artifact repositories automatically when new container images are pushed — no manual gcloud commands needed.
Use traffic splitting to deploy new versions to 10% of traffic first to test behavior in production with real users.
Gradually increase traffic (20%, 50%, 100%) as you gain confidence that the new version is performing well.
Create a second revision with new code and test it thoroughly before switching any traffic to it.
Switch 100% traffic instantly once you confirm the new version is working correctly — if issues arise, roll back just as fast.
Automate setting environment variables, memory limits, and concurrency limits based on your environment (dev, staging, prod) without manual edits.
Use Cloud Run Jobs to automate batch tasks (cleanup, reporting, data sync) on a schedule without managing Kubernetes or cron infrastructure.
Cequence AI Gateway