MCP Gemini Google Search
M

MCP Gemini Google Search

A server based on the MCP protocol that utilizes the built-in Google Search function of Gemini to provide real-time web search services, supporting two access methods, Google AI Studio and Vertex AI.
2.5 points
7.1K

What is MCP Gemini Google Search?

MCP Gemini Google Search is a Model Context Protocol (MCP) server that utilizes the built-in Google Search function of Gemini to provide users with real-time web search results and citation sources. It complies with the MCP standard protocol and supports multiple transmission methods.

How to use MCP Gemini Google Search?

Configure the API key or project information by setting environment variables, and then start the server. Users can call the search function through the command line or integrated tools to obtain real-time web information.

Applicable scenarios

Suitable for scenarios that require real-time web information queries, academic research, news summaries, etc. Particularly suitable for research and development work that requires citation sources.

Main features

Built-in Google Search function
Directly utilize the Google Search capabilities of Gemini without additional configuration of a search engine.
Real-time search and citation
Provide real-time web search results with source citations to ensure the traceability of information.
MCP protocol compatibility
Fully comply with the Model Context Protocol (MCP) standard to ensure compatibility with other systems.
Multi-platform support
Support stdio transmission and can run in various environments, including local development and cloud deployment.
Two API modes
Support two API modes, Google AI Studio and Vertex AI, to meet different needs.
Advantages
Provide real-time web search results to ensure the timeliness of information.
Built-in Google Search function without additional configuration.
Support multiple API modes to adapt to different usage scenarios.
Comply with the MCP standard protocol and are easy to integrate into existing systems.
Provide clear citation sources to enhance the credibility of information.
Limitations
Dependent on Google Search services and may be subject to API restrictions.
Require a valid API key or project configuration, and initial use may require some setup.
Support for certain regions or languages may be limited.
Performance may need to be optimized under high concurrency.
Search results for non-English content may not meet expectations.

How to use

Installation
Install the MCP Gemini Google Search server using npm.
Configure environment variables
Set the necessary environment variables according to the API type used (Google AI Studio or Vertex AI).
Start the server
Run the command to start the MCP Gemini Google Search server.
Call the search function
Call the search function through the command line or integrated tools and enter the query term to get the results.

Usage examples

Find the latest TypeScript features
Users want to learn about the latest TypeScript updates, including new features and improvements.
Search for artificial intelligence research papers
Researchers need to find recent research papers related to artificial intelligence.

Frequently Asked Questions

What prerequisites are required for MCP Gemini Google Search?
How to choose between Google AI Studio and Vertex AI?
Does MCP Gemini Google Search support Chinese?
How to debug MCP Gemini Google Search?

Related resources

GitHub repository
Project source code and documentation
Model Context Protocol (MCP)
Official documentation of the MCP protocol
Gemini API documentation
Explanation of the Google Search function of the Gemini API
Claude Code guide
Claude Code usage guide

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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