mcp-gemini-tutorial
The project is a tutorial on building MCP servers using Google's Gemini 2.0 model, integrating Brave Search for web and local searches. It emphasizes interoperability, modularity, and a clean architecture for AI-driven applications.
What is the purpose of MCP?
MCP provides a standardized way for AI models to interact with external tools and resources, enhancing interoperability and reducing integration complexity.
What are the prerequisites for setting up the MCP server?
You need Bun for fast TypeScript execution, a Brave Search API key, and a Google API key for Gemini access.
How can I extend the project with new tools?
You can add new tools by defining a schema, implementing the functionality, and registering it with the MCP server.
What are the main tools implemented in this MCP server?
The main tools are Web Search for general internet searches and Local Search for finding businesses and locations, both via Brave Search.
Where can I find more information about MCP?
You can refer to the official MCP documentation, Google Gemini API documentation, and Brave Search API documentation for more details.