What are the prerequisites to use Gemini MCP Server?
You need a Google Gemini API key and Go installed to build the binary. No additional dependencies are required.
Gemini MCP Server
@chew-z
Basic information
About Gemini MCP Server
MCP (Model Control Protocol) server integrating with Google's Gemini API
Gemini MCP Server config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryGemini MCP Server tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is Gemini MCP Server?
Gemini MCP Server is a Model Control Protocol (MCP) server that integrates with Google's Gemini API. Written in Go and compiled to a single self-contained binary, it provides tools for code analysis, general queries, and search with grounding. It is designed for developers using MCP-compatible clients like Claude Desktop.
How to use Gemini MCP Server?
Clone the repository and build the binary with go build -o mcp-gemini. Set the GEMINI_API_KEY environment variable and optionally specify a model. Configure the server in your MCP client (e.g., Claude Desktop) by adding a JSON entry with the binary path and environment variables (all config must be in the env section of the client configuration, not as system environment variables).
Key features of Gemini MCP Server
- Single self-contained binary with no dependencies
- Dynamic model access: fetches latest Gemini models at startup
- Advanced caching system with configurable TTL
- Seamless file integration with intelligent MIME detection
- Robust error handling, automatic retries, and graceful degradation
- Full support for code analysis, general queries, and search with grounding
Use cases of Gemini MCP Server
- Code analysis and review with file attachments and caching
- Creative writing with custom system prompts
- Factual research using Google Search integration
- Complex reasoning tasks with thinking mode enabled
- Multi-file project analysis with cost-efficient caching
Frequently asked questions
How do I configure Gemini MCP Server for Claude Desktop?
Add a JSON entry to your Claude Desktop configuration file (on macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`; on Windows: `%APPDATA%\Claude\claude_desktop_config.json`) with the binary path and all environment variables (e.g., `GEMINI_API_KEY`, `GEMINI_MODEL`) inside the `env` block. System environment variables are not passed to the client.
What tools does Gemini MCP Server provide?
It provides three tools: `gemini_ask` for code analysis and general queries with optional file context, `gemini_search` for grounded answers with Google Search, and `gemini_models` to list all available Gemini models with capabilities and caching support.
How does caching work in Gemini MCP Server?
Caching is enabled via the `use_cache` parameter and a configurable `cache_ttl` (e.g., `"30m"`). Files are processed once and cached, making subsequent queries faster and reducing API costs by an estimated 40–60% for extended reviews.
Which Gemini models are supported?
Models are dynamically fetched from the Google API at startup. Recommended models include `gemini-2.5-pro-exp-03-25` for reasoning, `gemini-2.0-flash-001` for cached tasks, and `gemini-2.5-flash-preview-04-17` for search.
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