MCPMarket
Sell SkillsPower Your AgentsConnect
  1. Home
  2. Servers
  3. Gemini Tutorial

Gemini Tutorial

GuiBibeaubyGuiBibeau
•
25
•
API Development
Learning & Documentation
Data Science & ML

Demonstrates how to build Model Context Protocol (MCP) servers with Google's Gemini 2.0 model.

Related MCPs

View more
  • neondatabase-labs

    Neon

    Enables natural language interaction with the Neon Management API and databases through the Model Context Protocol.

  • mendableai

    Firecrawl

    Empowers LLMs with advanced web scraping capabilities for content extraction, crawling, and search functionalities.

  • GLips

    Figma Context

    Provides AI coding agents with simplified Figma layout information via the Model Context Protocol.

Related Skills

View all
  • openclaw

    Diagram Maker & Visualizer

    Generates professional SVG, HTML, and Excalidraw diagrams for software architecture, system flows, and educational concepts.

  • openclaw

    GH Issues Auto-Fixer

    Automates the end-to-end GitHub issue lifecycle by spawning sub-agents to implement code fixes, open pull requests, and resolve review comments.

  • openclaw

    Discord Integration

    Manages Discord operations including messaging, reactions, and channel management directly through Claude.

MCPMarket

Discover MCP servers that connect MCP clients like Claude and Cursor to your favorite tools. Browse the MCP Market to get started.

Browse

  • MCP Search
  • MCP Servers
  • MCP Clients
  • Agent Skills
  • MCP Market Hub
  • Categories
  • What is an MCP server?
  • Model Context Protocol

Rankings

  • Top MCPs Today
  • Top Agent Skills Today
  • Top 100 Agent Skills
  • Top 100 MCP Servers

About

  • News
  • Submit
  • Contact

© 2026 MCP Market. All rights reserved.·Privacy·Terms

This tutorial provides a complete code example for building Model Context Protocol (MCP) servers using Google's Gemini 2.0 model. It showcases how to integrate external tools, such as Brave Search, into AI models through the standardized MCP interface, enabling interoperability, modularity, and a clear separation of concerns between model capabilities and tool functionalities. The project includes a basic client and a Gemini integration example, allowing users to explore the implementation and extend it with custom tools.

Key Features

01Integration with Google's Gemini 2.0 model
02Brave Search integration for web and local searches
03Demonstrates a flexible architecture for AI-powered applications
04Complete code example for building MCP servers
0520 GitHub stars
06Includes example clients and tests

Use Cases

01Connecting AI models to external tools and resources
02Building AI-powered applications with web search capabilities
03Implementing the Model Context Protocol (MCP) standard