Google Cloud Run MCP

Name: Google Cloud Run MCP
Author: Google

by Google

Official Google Cloud Run integration — deploy and manage serverless containers through AI

cloud Go Intermediate Self-hostable Verified

⭐ 300 stars 📅 Updated: 1mo ago

View on GitHub

Description

Official MCP server from Google Cloud Platform for managing Cloud Run services through AI assistants. Deploy containerized applications, manage service revisions, configure traffic splitting, view real-time logs, and monitor service health — all via natural language. Built in Go for performance, it wraps the Cloud Run API into MCP-compatible tools that enable AI-driven serverless container operations. Ideal for DevOps teams and developers who want to manage their Cloud Run deployments conversationally without leaving their AI coding environment.

✅ Best for

DevOps teams and developers managing Cloud Run deployments through AI-powered workflows

⏭️ Skip if

You deploy to other cloud providers or need broader GCP service management

💡 Use cases

Deploying containerized services to Cloud Run through AI assistants
Managing service revisions and rolling back deployments
Configuring traffic splitting for canary and blue-green deployments
Viewing real-time logs and monitoring service health

👍 Pros

✓ Official Google Cloud project with direct API access
✓ Built in Go for high performance and low resource usage
✓ Complete Cloud Run lifecycle management — deploy, update, monitor
✓ Integrates with existing gcloud authentication

👎 Cons

✗ Requires Google Cloud account with billing enabled
✗ Limited to Cloud Run — does not cover other GCP services
✗ Go runtime required for building from source

🔧 Exposed tools (5 tools)

Tool	Category	Description
deploy_service	deployment	Deploy a container image to Cloud Run
list_services	management	List all Cloud Run services in a project
manage_revisions	management	List, describe, or delete service revisions
view_logs	monitoring	View real-time logs for Cloud Run services
configure_traffic	networking	Configure traffic splitting between service revisions

⚡ Installation

Prerequisites:

• go v1.21+
• Google Cloud project with Cloud Run API enabled
• Authenticated gcloud CLI or service account credentials
• API key required

Check Claude Code documentation to configure this MCP server.

💡 Tips & tricks

Make sure your gcloud CLI is authenticated before starting the MCP server. Use traffic splitting tools for safe canary deployments — route a small percentage of traffic to new revisions before full rollout.

🔄 Alternatives

Azure MCP

Community-driven Azure integration for AI agents via MCP

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