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Google Cloud Compute Engine

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Google

Google 💚 MCP

PublisherGoogle
Repositorymcp
LanguageTypeScript
Forks
457
Stars
4.1K
Available tools
0
Transport typestreamable-http
Categories
Cloud InfrastructureDeveloper Tools
LicenseApache-2.0
Links
GitHub
  • Connect tools to AI workflows

    Google Cloud Compute Engine exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    4.1K stars and 457 forks from the linked repository.

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. Below are a few key remote servrs. For a comprehensive list of all remote MCP servers managed by Google in General Availability (GA) and public preview vist the supported products pager

Open-source MCP servers

You can run these open-source MCP servers locally, or deploy them to Google Cloud (see below).

💻 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.
  • Allstrides (/examples/allstrides): A sample app migrated from local to Cloud using remote MCP servers for Developer Knowledge, Cloud SQL and Cloud Run

📙 Resources

Run an MCP server in Google Cloud

🤝 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.

Installation

TypingMind
{
  "mcpServers": {
    "ComputeEngine": {
      "url": "https://compute.googleapis.com/mcp"
    }
  }
}

Use Google Cloud Compute Engine MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Google Cloud Compute Engine is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP connects through a hosted MCP server URL in TypingMind.

Add an MCP server URL

Use this when Google Cloud Compute Engine is already hosted remotely or your team wants one shared connector that multiple users can access.

1

Open MCP connectors

In TypingMind, go to Plugins, open MCP connectors, then choose Add URL.

  1. Open TypingMind in your browser.
  2. Go to Plugins.
  3. Open MCP connectors.
  4. Click Add URL.
TypingMind Add Custom MCP Server URL form
2

Paste the server URL

Enter https://compute.googleapis.com/mcp in the Server URL field. Add a connection name, description, icon, custom HTTP headers, or OAuth client settings if the server requires them.

  1. Paste https://compute.googleapis.com/mcp into the Server URL field.
  2. Enter a connection name for Google Cloud Compute Engine.
  3. Add a description and icon if you want it to be easier to identify.
  4. Add custom HTTP headers or OAuth client details if the server requires authentication.
3

Create the connection

Click Create connection, then return to the Plugins list and confirm the new MCP connection is active.

  1. Click Create connection.
  2. Return to the MCP connectors list.
  3. Confirm the Google Cloud Compute Engine connection appears as active.
  4. Refresh the plugin list if the connection does not appear immediately.
4

Switch models without reconnecting

Start a chat with your preferred model, enable the Google Cloud Compute Engine tools from Plugins, and switch to another model whenever needed. The MCP connection stays available to the TypingMind workspace.

  1. Start a new chat in TypingMind.
  2. Select the AI model you want to use.
  3. Enable the Google Cloud Compute Engine tools from Plugins.
  4. Ask the model to use the tool when needed.
  5. Switch to another AI model and reuse the same MCP connection.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Google Cloud Compute Engine to help me with this task?
Google Cloud Compute Engine
Sure. I read it.
Here is what I found using Google Cloud Compute Engine.

Frequently asked questions

What is the Google Cloud Compute Engine MCP server used for?

Google Cloud Compute Engine is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Google Cloud Compute Engine MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Google Cloud Compute Engine with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Google Cloud Compute Engine MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Google Cloud Compute Engine connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Google Cloud Compute Engine MCP to TypingMind?

Google Cloud Compute Engine can be connected in TypingMind by adding its hosted MCP server URL. This is useful when you want a remote MCP connection that is available from your TypingMind workspace.

What tools does Google Cloud Compute Engine MCP provide in TypingMind?

Google Cloud Compute Engine exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Google Cloud Compute Engine MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Google Cloud Compute Engine requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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