Google MCP facilitates connecting AI agents with real-world data and services through a standardized protocol. This repository lists official and open-source Model Context Protocol (MCP) servers, offering a way for agents to access information from services like BigQuery, Cloud Storage, and Google Maps. MCP provides a structured interface, simplifying integration and enabling more informed and context-aware AI applications.
The project offers a comprehensive list of both official Google-managed servers and a wide range of community-contributed open-source servers. It includes detailed documentation on deploying MCP servers to Google Cloud and provides practical examples for agent development. The architecture enables seamless integration with various Google services and external tools.
- Remote MCP Servers: A curated list of official Google-managed MCP servers for various services like BigQuery, Cloud Storage, and GCE, offering readily available connectivity.
- Open-source MCP Servers: A collection of open-source MCP servers for tools like Gemini CLI, Firebase, and Kubernetes Engine, fostering community development and wider adoption.
- Deployment Resources: Comprehensive documentation and codelabs for hosting MCP servers on Google Cloud Run and Google Kubernetes Engine (GKE), simplifying deployment processes.
- Agent Examples: Practical examples, like 'Launch My Bakery,' showcase how to effectively utilize MCP servers within AI agent workflows, demonstrating real-world applications.
- Contribute to the Project: Encourages community contributions through bug reports, feature requests, and code submissions, fostering collaborative development.
The project is actively maintained with recent commits and regular updates to the list of supported servers and deployment documentation. The extensive documentation and community involvement indicate a stable and evolving ecosystem.
This project benefits developers building AI agents by providing a standardized way to access Google services and external tools. Real-world use cases include creating agents that can query databases, access cloud storage, and leverage mapping data. MCP simplifies the integration process, enabling agents to function with richer context and capabilities compared to manual data retrieval methods.