GCP MCP Server

# 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 1. Clone the repository: ```bash git clone https://github.com/yourusername/gcp-mcp-server.git cd gcp-mcp-server ``` 2. Set up a virtual environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` 4. Configure your GCP credentials: ```bash # 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: ```bash cp .env.example .env # Edit .env with your configuration ``` ### Running the Server Start the MCP server: ```bash python main.py ``` For development and testing: ```bash # 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: ```bash # 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 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](services/README.md) 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](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: ```json "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.