What is MCP with Gemini Tutorial?
The MCP with Gemini Tutorial is a comprehensive guide for building Model Context Protocol (MCP) servers using Google's Gemini 2.0 model, enabling seamless integration of AI models with external tools and resources.
How to use the MCP with Gemini Tutorial?
To use the tutorial, clone the repository, install the necessary dependencies, set up your environment with API keys, and run the provided example clients to see the MCP server in action.
Key features of the MCP with Gemini Tutorial?
- Detailed instructions for building an MCP server with Brave Search integration.
- Example clients demonstrating the use of the MCP server.
- Modular architecture allowing easy addition of new tools.
Use cases of the MCP with Gemini Tutorial?
- Building AI-powered applications that require external tool integration.
- Creating custom tools for specific functionalities within the MCP framework.
- Enhancing AI models with real-time data access through the MCP server.
FAQ from the MCP with Gemini Tutorial?
- What is Model Context Protocol (MCP)?
MCP is an open standard that allows AI models to access external tools and resources seamlessly.
- What are the prerequisites for this tutorial?
You need Bun for TypeScript execution, Brave Search API key, and Google API key for Gemini access.
- Can I add my own tools to the MCP server?
Yes! You can define new tools, implement their functionality, and register them with the MCP server.
MCP with Gemini Tutorial
This repository contains the complete code for the tutorial on building Model Context Protocol (MCP) servers with Google's Gemini 2.0 model, as described in this blog post.
What is Model Context Protocol (MCP)?
MCP is an open standard developed by Anthropic that enables AI models to seamlessly access external tools and resources. It creates a standardized way for AI models to interact with tools, access the internet, run code, and more, without needing custom integrations for each tool or model.
Key benefits include:
- Interoperability: Any MCP-compatible model can use any MCP-compatible tool
- Modularity: Add or update tools without changing model integrations
- Standardization: Consistent interface reduces integration complexity
- Separation of Concerns: Clean division between model capabilities and tool functionality
Project Overview
This tutorial demonstrates how to:
- Build a complete MCP server with Brave Search integration
- Connect it to Google's Gemini 2.0 model
- Create a flexible architecture for AI-powered applications
Getting Started
Prerequisites
- Bun (for fast TypeScript execution)
- Brave Search API key
- Google API key for Gemini access
Installation
# Clone the repository
git clone https://github.com/GuiBibeau/mcp-gemini-tutorial.git
cd mcp-tutorial
# Install dependencies
bun install
Environment Setup
Create a .env file with your API keys:
BRAVE_API_KEY="your_brave_api_key"
GOOGLE_API_KEY="your_google_api_key"
Usage
Running the Basic Client
bun examples/basic-client.ts
Running the Gemini Integration
bun examples/gemini-tool-function.ts
Project Structure
src/- Core implementation of the MCP server and toolsexamples/- Example clients demonstrating how to use the MCP servertests/- Test files for the project
Tools Implemented
This MCP server exposes two main tools:
- Web Search: For general internet searches via Brave Search
- Local Search: For finding businesses and locations via Brave Search
Extending the Project
You can add your own tools by:
- Defining a new tool with a schema
- Implementing the functionality
- Registering it with the MCP server
Learn More
License
MIT
This project was created using bun init in bun v1.1.37. Bun is a fast all-in-one JavaScript runtime.
