Gemini Google Search
Provides Google Search functionality for AI models using Gemini's built-in Grounding with Google Search feature, returning real-time web search results with source citations.
What is Gemini Google Search?
Gemini Google Search is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to provides google search functionality for ai models using gemini's built-in grounding with google search feature, returning real-time web search results with source citations.
Provides Google Search functionality for AI models using Gemini's built-in Grounding with Google Search feature, returning real-time web search results with source citations.
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- google_search
Use Cases
Maintainer
Works with
Installation
NPM
npx -y mcp-gemini-google-searchManual Installation
npx -y mcp-gemini-google-searchConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Gemini Google Search
MCP Gemini Google Search provides real-time Google Search capabilities to any MCP-compatible AI client by routing queries through Google's Gemini models with built-in Search Grounding. Instead of using a separate search API, it leverages Gemini's native ability to ground responses in live web results, returning search findings with source citations attached. This gives Claude Desktop, Cursor, and other MCP clients accurate, up-to-date web information without requiring a standalone Bing or SerpAPI subscription.
Prerequisites
- Node.js 18 or later
- Google AI Studio API key (from aistudio.google.com) OR a Google Cloud Project with Vertex AI enabled
- npm or npx available in your PATH
- An MCP-compatible client such as Claude Desktop or Claude Code
Install the package
Install mcp-gemini-google-search globally via npm for use as a persistent MCP server command.
npm install -g mcp-gemini-google-searchObtain a Gemini API key
Visit Google AI Studio at aistudio.google.com, sign in with your Google account, and create an API key under 'Get API key'. Copy the key for use in configuration.
Configure Claude Desktop with Google AI Studio
Add the MCP server to Claude Desktop's config file using your GEMINI_API_KEY. The GEMINI_MODEL variable is optional and defaults to gemini-2.5-flash.
Alternative: Configure with Vertex AI
If you prefer using Google Cloud's Vertex AI instead of AI Studio, set the GEMINI_PROVIDER, VERTEX_PROJECT_ID, and VERTEX_LOCATION environment variables.
Add via Claude Code CLI (quickest setup)
The fastest way to register the server with Claude Code is via the 'claude mcp add' command, which handles config file editing automatically.
claude mcp add gemini-google-search \
-e GEMINI_API_KEY="your-api-key-here" \
-- npx mcp-gemini-google-searchGemini Google Search Examples
Client configuration
Claude Desktop configuration for the Gemini Google Search MCP server using Google AI Studio credentials.
{
"mcpServers": {
"gemini-google-search": {
"command": "npx",
"args": ["-y", "mcp-gemini-google-search"],
"env": {
"GEMINI_API_KEY": "your-gemini-api-key",
"GEMINI_MODEL": "gemini-2.5-flash"
}
}
}
}Vertex AI client configuration
Alternative configuration using Google Cloud Vertex AI instead of Google AI Studio.
{
"mcpServers": {
"gemini-google-search": {
"command": "npx",
"args": ["-y", "mcp-gemini-google-search"],
"env": {
"GEMINI_PROVIDER": "vertex",
"VERTEX_PROJECT_ID": "your-gcp-project-id",
"VERTEX_LOCATION": "us-central1",
"GEMINI_MODEL": "gemini-2.5-flash"
}
}
}
}Prompts to try
Once connected, the google_search tool is available for real-time web queries with citations.
- "Search for the latest news about the 2025 Nobel Prize winners."
- "What is the current weather forecast for Tokyo this week?"
- "Find the most recent benchmark results comparing Claude and GPT-4."
- "Search for Python 3.13 release notes and summarize the key new features."
- "What happened in the tech industry yesterday?"Troubleshooting Gemini Google Search
API key rejected with a 400 or 403 error
Verify your GEMINI_API_KEY is correct and was generated from Google AI Studio (aistudio.google.com), not from Google Cloud Console directly. Ensure the key has not been restricted to specific APIs — Search Grounding requires the Generative Language API to be enabled.
Search returns no citations or grounding is missing
Grounding with Google Search is a Gemini-specific feature and requires the model to support it. If results lack citations, ensure GEMINI_MODEL is set to a model that supports grounding (e.g., gemini-2.5-flash or gemini-2.0-flash). Experimental models may not support grounding.
Windows: npx command fails to launch the server
On Windows, wrap the command with cmd: set the command to 'cmd' and args to ['/c', 'npx', 'mcp-gemini-google-search'] in the MCP config file.
Frequently Asked Questions about Gemini Google Search
What is Gemini Google Search?
Gemini Google Search is a Model Context Protocol (MCP) server that provides google search functionality for ai models using gemini's built-in grounding with google search feature, returning real-time web search results with source citations. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Gemini Google Search?
Install via npm with the command: npx -y mcp-gemini-google-search. Then add the server configuration to your AI client's JSON config file (e.g., claude_desktop_config.json or .cursor/mcp.json).
Which AI clients work with Gemini Google Search?
Gemini Google Search works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Gemini Google Search free to use?
Yes, Gemini Google Search is open source and available under the Apache 2.0 license. You can use it freely in both personal and commercial projects.
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Xiaohongshu
★ 13.7kMCP for xiaohongshu.com
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