Use This MCP server To

Learn to build MCP servers with Google Gemini 2.0 Demonstrate MCP interoperability with Gemini model Develop modular AI tool integrations using MCP Standardize AI model and tool communication Prototype MCP server implementations for AI workflows

README

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 tools
examples/ - Example clients demonstrating how to use the MCP server
tests/ - 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

Official MCP Documentation
Google Gemini API Documentation
Brave Search API Documentation

License

MIT

This project was created using bun init in bun v1.1.37. Bun is a fast all-in-one JavaScript runtime.

mcp-gemini-tutorial FAQ

How does this tutorial help with MCP server development?

It provides complete code and guidance for building MCP servers using Google Gemini 2.0, illustrating best practices and standards.

Can I use this tutorial to build MCP servers for other models?

Yes, the principles and code can be adapted for other MCP-compatible models like OpenAI GPT-4 and Anthropic Claude.

What prerequisites are needed to follow this tutorial?

Basic knowledge of MCP concepts, familiarity with Google Gemini API, and programming experience in relevant languages.

Does this tutorial cover deployment of MCP servers?

It focuses on building and understanding MCP servers; deployment details may require additional resources.

Is the tutorial code open source and customizable?

Yes, the repository is open source, allowing customization and extension for specific MCP server needs.

How does MCP improve AI model integrations?

MCP standardizes tool access, enabling models like Gemini, GPT-4, and Claude to interact with external resources seamlessly.