This is not a Ready MCP Server

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

Clone the repository:
git clone https://github.com/yourusername/gcp-mcp-server.git cd gcp-mcp-server
Set up a virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
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"
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:

Environment Variable	Description	Default
`GCP_PROJECT_ID`	Default GCP project ID	None (required)
`GCP_DEFAULT_LOCATION`	Default region/zone	`us-central1`
`MCP_SERVER_PORT`	Server port	`8080`
`LOG_LEVEL`	Logging level	`INFO`

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

Development

Adding a New GCP Service

Create a new file in the services/ directory
Implement the service following the pattern in existing services
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.

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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:

Place your GCP service account key file as service-account.json in the same directory
Install the MCP package: pip install "mcp[cli]"
Install the required GCP package: pip install google-cloud-run
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.

Integrations