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GCP MCP Server

A comprehensive Model Context Protocol (MCP) server implementation for Google Cloud Platform (GCP) services, enabling AI assistants to interact with and manage GCP resources through a standardized interface.

Overview

GCP MCP Server provides AI assistants with capabilities to:

• Query GCP Resources: Get information about your cloud infrastructure

• Manage Cloud Resources: Create, configure, and manage GCP services

• Receive Assistance: Get AI-guided help with GCP configurations and best practices

The implementation follows the MCP specification to enable AI systems to interact with GCP services in a secure, controlled manner.

Supported GCP Services

This implementation includes support for the following GCP services:

• Artifact Registry: Container and package management

• BigQuery: Data warehousing and analytics

• Cloud Audit Logs: Logging and audit trail analysis

• Cloud Build: CI/CD pipeline management

• Cloud Compute Engine: Virtual machine instances

• Cloud Monitoring: Metrics, alerting, and dashboards

• Cloud Run: Serverless container deployments

• Cloud Storage: Object storage management

Architecture

The project is structured as follows:

gcp-mcp-server/
├── core/            # Core MCP server functionality auth context logging_handler security 
├── prompts/         # AI assistant prompts for GCP operations
├── services/        # GCP service implementations
│   ├── README.md    # Service implementation details
│   └── ...          # Individual service modules
├── main.py          # Main server entry point
└── ...

Key components:

• Service Modules: Each GCP service has its own module with resources, tools, and prompts

• Client Instances: Centralized client management for authentication and resource access

• Core Components: Base functionality for the MCP server implementation

Getting Started

Prerequisites

• Python 3.10+

• GCP project with enabled APIs for the services you want to use

• Authenticated GCP credentials (Application Default Credentials recommended)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/gcp-mcp-server.git
   cd gcp-mcp-server

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure your GCP credentials:

   # Using gcloud
   gcloud auth application-default login
   
   # Or set GOOGLE_APPLICATION_CREDENTIALS
   export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"

5. Set up environment variables:

   cp .env.example .env
   # Edit .env with your configuration

Running the Server

Start the MCP server:

python main.py

For development and testing:

# Development mode with auto-reload
python main.py --dev

# Run with specific configuration
python main.py --config config.yaml

Docker Deployment

Build and run with Docker:

# Build the image
docker build -t gcp-mcp-server .

# Run the container
docker run -p 8080:8080 -v ~/.config/gcloud:/root/.config/gcloud gcp-mcp-server

Configuration

The server can be configured through environment variables or a configuration file:

See .env.example for a complete list of configuration options.

Development

Adding a New GCP Service

1. Create a new file in the services/ directory

2. Implement the service following the pattern in existing services

3. Register the service in main.py

See the services README for detailed implementation guidance.

Security Considerations

• The server uses Application Default Credentials for authentication

• Authorization is determined by the permissions of the authenticated identity

• No credentials are hardcoded in the service implementations

• Consider running with a service account with appropriate permissions

Contributing

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

1. Fork the repository

2. Create your feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add some amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Google Cloud Platform team for their comprehensive APIs

• Model Context Protocol for providing a standardized way for AI to interact with services

Using the Server

To use this server:

1. Place your GCP service account key file as service-account.json in the same directory

2. Install the MCP package: pip install "mcp[cli]"

3. Install the required GCP package: pip install google-cloud-run

4. Run: mcp dev gcp_cloudrun_server.py

Or install it in Claude Desktop:

mcp install gcp_cloudrun_server.py --name "GCP Cloud Run Manager"

MCP Server Configuration

The following configuration can be added to your configuration file for GCP Cloud Tools:

"mcpServers": {
  "GCP Cloud Tools": {
    "command": "uv",
    "args": [
      "run",
      "--with",
      "google-cloud-artifact-registry>=1.10.0",
      "--with",
      "google-cloud-bigquery>=3.27.0",
      "--with",
      "google-cloud-build>=3.0.0",
      "--with",
      "google-cloud-compute>=1.0.0",
      "--with",
      "google-cloud-logging>=3.5.0",
      "--with",
      "google-cloud-monitoring>=2.0.0",
      "--with",
      "google-cloud-run>=0.9.0",
      "--with",
      "google-cloud-storage>=2.10.0",
      "--with",
      "mcp[cli]",
      "--with",
      "python-dotenv>=1.0.0",
      "mcp",
      "run",
      "C:\\Users\\enes_\\Desktop\\mcp-repo-final\\gcp-mcp\\src\\gcp-mcp-server\\main.py"
    ],
    "env": {
      "GOOGLE_APPLICATION_CREDENTIALS": "C:/Users/enes_/Desktop/mcp-repo-final/gcp-mcp/service-account.json",
      "GCP_PROJECT_ID": "gcp-mcp-cloud-project",
      "GCP_LOCATION": "us-east1"
    }
  }
}

Configuration Details

This configuration sets up an MCP server for Google Cloud Platform tools with the following:

• Command: Uses uv package manager to run the server

• Dependencies: Includes various Google Cloud libraries (Artifact Registry, BigQuery, Cloud Build, etc.)

• Environment Variables:

  • GOOGLE_APPLICATION_CREDENTIALS: Path to your GCP service account credentials

  • GCP_PROJECT_ID: Your Google Cloud project ID

  • GCP_LOCATION: GCP region (us-east1)

Usage

Add this configuration to your MCP configuration file to enable GCP Cloud Tools functionality.