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eniayomi

A Model Context Protocol (MCP) server that enables AI assistants like Claude to interact with your Google Cloud Platform environment. This allows for natural language querying and management of your GCP resources during conversations.

Publishereniayomi
Repositorygcp-mcp
LanguageTypeScript
Forks
33
Stars
198
Available tools
9
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

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

  • 9 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

    198 stars and 33 forks from the linked repository.

GCP MCP

A Model Context Protocol (MCP) server that enables AI assistants like Claude to interact with your Google Cloud Platform environment. This allows for natural language querying and management of your GCP resources during conversations.

GCP MCP Demo

Features

  • 🔍 Query and modify GCP resources using natural language
  • ☁️ Support for multiple GCP projects
  • 🌐 Multi-region support
  • 🔐 Secure credential handling (no credentials are exposed to external services)
  • 🏃‍♂️ Local execution with your GCP credentials
  • 🔄 Automatic retries for improved reliability

Prerequisites

  • Node.js
  • Claude Desktop/Cursor/Windsurf
  • GCP credentials configured locally (application default credentials)

Installation

  1. Clone the repository:
bash
git clone https://github.com/eniayomi/gcp-mcp
cd gcp-mcp
  1. Install dependencies:
bash
npm install

Configuration

Claude Desktop

  1. Open Claude desktop app and go to Settings -> Developer -> Edit Config

  2. Add the following entry to your claude_desktop_config.json:

via npm:

json
{
  "mcpServers": {
    "gcp": {
      "command": "sh",
      "args": ["-c", "npx -y gcp-mcp"]
    }
  }
}

If you installed from source:

json
{
  "mcpServers": {
    "gcp": {
      "command": "npm",
      "args": [
        "--silent",
        "--prefix",
        "/path/to/gcp-mcp",
        "start"
      ]
    }
  }
}

Replace /path/to/gcp-mcp with the actual path to your project directory if using source installation.

Cursor

  1. Open Cursor and go to Settings (⌘,)
  2. Navigate to AI -> Model Context Protocol
  3. Add a new MCP configuration:
json
{
  "gcp": {
    "command": "npx -y gcp-mcp"
  }
}

Windsurf

  1. Open ~/.windsurf/config.json (create if it doesn't exist)
  2. Add the MCP configuration:
json
{
  "mcpServers": {
    "gcp": {
      "command": "npx -y gcp-mcp"
    }
  }
}

GCP Setup

  1. Set up GCP credentials:

    • Set up application default credentials using gcloud auth application-default login
  2. Refresh your AI assistant (Claude Desktop/Cursor/Windsurf)

Usage

Start by selecting a project or asking questions like:

  • "List all GCP projects I have access to"
  • "Show me all Cloud SQL instances in project X"
  • "What's my current billing status?"
  • "Show me the logs from my Cloud Run services"
  • "List all GKE clusters in us-central1"
  • "Show me all Cloud Storage buckets in project X"
  • "What Cloud Functions are deployed in us-central1?"
  • "List all Cloud Run services"
  • "Show me BigQuery datasets and tables"

Available Tools

  1. run-gcp-code: Execute GCP API calls using TypeScript code
  2. list-projects: List all accessible GCP projects
  3. select-project: Select a GCP project for subsequent operations
  4. get-billing-info: Get billing information for the current project
  5. get-cost-forecast: Get cost forecast for the current project
  6. get-billing-budget: Get billing budgets for the current project
  7. list-gke-clusters: List all GKE clusters in the current project
  8. list-sql-instances: List all Cloud SQL instances in the current project
  9. get-logs: Get Cloud Logging entries for the current project

Example Interactions

  1. List available projects:
List all GCP projects I have access to
  1. Select a project:
Use project my-project-id
  1. Check billing status:
What's my current billing status?
  1. View logs:
Show me the last 10 log entries from my project

Supported Services

  • Google Compute Engine
  • Cloud Storage
  • Cloud Functions
  • Cloud Run
  • BigQuery
  • Cloud SQL
  • Google Kubernetes Engine (GKE)
  • Cloud Logging
  • Cloud Billing
  • Resource Manager
  • More coming soon...

Troubleshooting

To see logs:

bash
tail -n 50 -f ~/Library/Logs/Claude/mcp-server-gcp.log

Common issues:

  1. Authentication errors: Ensure you've run gcloud auth application-default login
  2. Permission errors: Check IAM roles for your account
  3. API errors: Verify that required APIs are enabled in your project

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "gcp": {
      "command": "npx",
      "args": [
        "-y",
        "gcp-mcp"
      ]
    }
  }
}

Available Tools

  • run-gcp-code

    Run GCP code

  • list-projects

    List all GCP projects accessible with current credentials

  • select-project

    Selects GCP project to use for subsequent interactions

  • get-billing-info

    Get billing information for the current project

  • get-cost-forecast

    Get cost forecast for the current project

  • get-billing-budget

    Get billing budgets for the current project

  • list-gke-clusters

    List all GKE clusters in the current project

  • list-sql-instances

    List all Cloud SQL instances in the current project

  • get-logs

    Get Cloud Logging entries for the current project

Use Google Cloud Platform MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Google Cloud Platform is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Google Cloud Platform as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Google Cloud Platform MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "google-cloud-platform": {
      "command": "npx",
      "args": [
        "-y",
        "gcp-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Google Cloud Platform MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Google Cloud Platform MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Google Cloud Platform to help me with this task?
Google Cloud Platform
Sure. I read it.
Here is what I found using Google Cloud Platform.

Frequently asked questions

What is the Google Cloud Platform MCP server used for?

Google Cloud Platform 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 Platform MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Google Cloud Platform 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 Platform 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 Platform 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 Platform MCP to TypingMind?

Google Cloud Platform runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Google Cloud Platform MCP provide in TypingMind?

Google Cloud Platform exposes 9 MCP tools 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 Platform 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 Platform requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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