Overview
What is Gemini MCP Server?
It implements a Model Context Protocol (MCP) server for Google Search, enabling integration with AI assistants and other MCP-compatible clients.
How to use Gemini MCP Server?
Install dependencies with npm install, set up your Google Custom Search API key in a .env file as GEMINI_API_KEY, then run a test search via npx ts-node src/test.ts or integrate it as an MCP server in your client configuration (e.g., claude_desktop_config.json).
Key features of Gemini MCP Server
- Provides a
googleSearchContenttool for web search results (title, link, snippet). - Provides a
googleSearchImagestool for image search (Markdown image link). - Built on Node.js 18+ with npm package management.
- Works with any MCP-compatible client.
- Simple
.env‑based credential management.
Use cases of Gemini MCP Server
- AI assistants retrieving real‑time web search results.
- Fetching image links for visual content in conversations.
- Integrating Google Search capabilities into custom MCP pipelines.
- Testing and prototyping MCP server configurations.
FAQ from Gemini MCP Server
What are the prerequisites?
Node.js 18 or newer and npm are required.
How do I get the required API key?
Obtain a Google Custom Search API key from the Google Custom Search JSON API documentation and place it in a .env file as GEMINI_API_KEY.
What tools does the server provide?
Two tools: googleSearchContent (web search returning titles, links, and snippets) and googleSearchImages (image search returning a Markdown image link for the first result).
How can I integrate this server with Claude or another MCP client?
Add an entry to your MCP settings file (e.g., claude_desktop_config.json) with the command npx ts-node src/index.ts and the GEMINI_API_KEY environment variable set to your key.
What should I do if search fails?
Verify that your API key is correctly set in the .env file or environment, all dependencies are installed (npm install), Node.js 18+ is used, and the server has been restarted after any code changes.