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.
@chew-z
基本情報
MCP (Model Control Protocol) server integrating with Google's Gemini API
ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
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.
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).
You need a Google Gemini API key and Go installed to build the binary. No additional dependencies are required.
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.
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.
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.
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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