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Gemini MCP Server

@aliargun

リポジトリ

Gemini MCP Server について

MCP server implementation for Google's Gemini API

#gemini#mcp-server#google-api

基本情報

カテゴリ

AI とエージェント

ライセンス

MIT

ランタイム

node

トランスポート

stdio

公開者

aliargun

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "gemini": {
      "command": "npx",
      "args": [
        "-y",
        "github:aliargun/mcp-server-gemini"
      ],
      "env": {
        "GEMINI_API_KEY": "your_api_key_here"
      }
    }
  }
}

ツール

ツールは検出されませんでした

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。

概要

What is Gemini MCP Server?

Gemini MCP Server is a Model Context Protocol (MCP) server that integrates Google’s latest Gemini AI models—including Gemini 2.5 with thinking capabilities—into any MCP-compatible development environment (e.g., Claude Desktop, Cursor, Windsurf). It provides tools for text generation, image analysis, token counting, model listing, embeddings, and self-documentation.

How to use Gemini MCP Server?

Configure your MCP client with the npx command npx -y github:aliargun/mcp-server-gemini and set the GEMINI_API_KEY environment variable. Once connected, use natural language in your client to invoke Gemini capabilities (e.g., “Use Gemini to review this code” or “Analyze this image with Gemini”).

Key features of Gemini MCP Server

  • Six built-in tools: text generation, image analysis, token counting, model listing, embeddings, and help
  • Supports latest Gemini 2.5 models with thinking, JSON mode, and Google Search grounding
  • Full MCP protocol via stdio communication for seamless client integration
  • Self-documenting help system accessible from within the editor
  • Modern TypeScript and ESM implementation
  • Configurable system instructions and conversation memory

Use cases of Gemini MCP Server

  • Complex reasoning tasks using Gemini 2.5 Pro’s 2M token context
  • Code review and analysis with adjustable temperature and model selection
  • Image analysis (vision) within an editor workflow
  • Research with grounding to incorporate Google Search results
  • Token counting and model listing for capacity planning

FAQ from Gemini MCP Server

What models are supported?

The server supports Gemini 2.5 Pro (2M tokens, thinking, JSON, grounding), Gemini 2.5 Flash (1M tokens, thinking, JSON, grounding), Gemini 2.5 Flash Lite (1M tokens, thinking, JSON), Gemini 2.0 Flash (1M tokens, JSON, grounding), and Gemini 1.5 Pro (2M tokens, JSON).

How do I get and set up an API key?

Obtain an API key from Google AI Studio (makersuite.google.com/app/apikey). Set it as the environment variable GEMINI_API_KEY in your MCP client configuration. Never commit the key to version control.

Which MCP clients are supported?

The server works with any MCP-compatible client that supports stdio transport, including Claude Desktop, Cursor, and Windsurf. Configuration examples for each are provided in the README.

What transport does the server use?

The server uses stdio transport, communicating via standard input/output with the MCP client. No HTTP or WebSocket transport is mentioned.

Are there any known limitations?

The README does not list specific limitations, but notes that API keys must be kept secure, and common issues can be resolved by checking client logs and internet connectivity. Troubleshooting guidance is available in the dedicated docs.

コメント

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