Google Cloud Compute Engine▌
by google
Explore MCP servers for Google Compute Engine. Integrate model context protocol solutions to streamline GCE app developm
Discover official and open-source Model Context Protocol (MCP) servers from Google. This project provides an up-to-date directory of MCP servers for Google services like Compute Engine. Explore examples and resources that help you build, integrate, and extend intelligent agents using Google's ecosystem of MCP solutions—all designed to streamline context-aware app development and experimentation.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Cloud developers building on Google Cloud Platform
- / Data engineers working with Google's database services
- / DevOps teams managing GCP infrastructure
- / Developers integrating Google services into AI agents
capabilities
- / Manage Compute Engine instances and resources
- / Query BigQuery datasets and tables
- / Access Cloud SQL databases (MySQL, PostgreSQL, SQL Server)
- / Control Kubernetes Engine clusters
- / Query Firestore and Spanner databases
- / Access Google Developer documentation
what it does
Provides access to Google's official MCP servers for cloud services like Compute Engine, BigQuery, and Kubernetes Engine. Offers both managed remote servers and open-source versions you can deploy yourself.
about
Google Cloud Compute Engine is an official MCP server published by google that provides AI assistants with tools and capabilities via the Model Context Protocol. Explore MCP servers for Google Compute Engine. Integrate model context protocol solutions to streamline GCE app developm It is categorized under cloud infrastructure, developer tools.
how to install
You can install Google Cloud Compute Engine 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 supports remote connections over HTTP, so no local installation is required.
license
Apache-2.0
Google Cloud Compute Engine 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.
readme
google/mcp
This repository contains a list of Google's official Model Context Protocol (MCP) servers, guidance on how to deploy MCP servers to Google Cloud, and examples to get started.
⚡ Google MCP Servers
Remote MCP servers
These remote MCP servers are managed by Google, and are available via endpoint. This list will be kept up-to-date as more remote servers become available.
- AlloyDB for PostgreSQL
- BigQuery
- Bigtable
- Cloud Resource Manager
- Cloud SQL for MySQL
- Cloud SQL for PostgreSQL
- Cloud SQL for SQL Server
- Compute Engine (GCE)
- Developer Knowledge API (Google Developer Documentation)
- Firestore
- Google Maps (Grounding Lite)
- Google Security Operations (Chronicle)
- Kubernetes Engine (GKE)
- Spanner
Open-source MCP servers
You can run these open-source MCP servers locally, or deploy them to Google Cloud (see below).
- Google Workspace, including Google Docs, Sheets, Slides, Calendar, and Gmail. (Gemini CLI extension)
- Firebase (Gemini CLI extension)
- Cloud Run (Gemini CLI Extension)
- Go
- Google Analytics
- MCP Toolbox for Databases, including BigQuery, Cloud SQL, AlloyDB, Spanner, Firestore, and more.
- Google Cloud Storage
- Genmedia, including Imagen and Veo models.
- Kubernetes Engine (GKE)
- Google Cloud Security, including Security Command Center, Chronicle, and more.
- gcloud CLI
- Google Cloud Observability
- Flutter/Dart
- Google Maps Platform Code Assist toolkit
- Chrome DevTools
💻 Examples
- Launch My Bakery (
/examples/launchmybakery): A sample agent built with Agent Development Kit (ADK) that uses remote MCP servers for Google Maps and BigQuery.
📙 Resources
Run an MCP server in Google Cloud
- Documentation - Host MCP Servers on Cloud Run
- Blog Post - Build and Deploy a Remote MCP Server to Google Cloud Run in Under 10 Minutes
- MCP Toolbox for Databases - Deploy to Cloud Run, Deploy to Google Kubernetes Engine (GKE)
- Blog post - Announcing MCP support for Apigee (Turnkey MCP hosting for Apigee-hosted APIs)
- “Tools Make an Agent” - Blog and Codelab
- Codelab - How to deploy a secure MCP server on Cloud Run
- Codelab - "Agent Verse" - Architecting Multi-agent Systems
🤝 Contributing
We welcome contributions to this repository, including bug reports, feature requests, documentation improvements, and code contributions. Please see our Contributing Guidelines to get started.
📃 License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
Disclaimers
This is not an officially supported Google product. This project is intended for demonstration purposes only.
This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.
FAQ
- What is the Google Cloud Compute Engine MCP server?
- Google Cloud Compute Engine is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Google Cloud Compute Engine?
- This profile displays 70 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.5★★★★★70 reviews- ★★★★★Naina Martin· Dec 16, 2024
Strong directory entry: Google Cloud Compute Engine surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Sophia Sharma· Dec 12, 2024
We evaluated Google Cloud Compute Engine against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chinedu Anderson· Dec 12, 2024
Useful MCP listing: Google Cloud Compute Engine is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Benjamin Choi· Dec 12, 2024
We wired Google Cloud Compute Engine into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Kiara Robinson· Dec 8, 2024
I recommend Google Cloud Compute Engine for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Chaitanya Patil· Dec 4, 2024
Google Cloud Compute Engine has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Piyush G· Nov 23, 2024
We evaluated Google Cloud Compute Engine against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Kaira Diallo· Nov 7, 2024
Google Cloud Compute Engine is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Rahul Santra· Nov 3, 2024
According to our notes, Google Cloud Compute Engine benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★William Thomas· Nov 3, 2024
Google Cloud Compute Engine has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
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