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Gemini MCP Server

@aliargun

リポジトリ

基本情報

カテゴリ

AI とエージェント

ライセンス

MIT

トランスポート

stdio

作者

aliargun

Gemini MCP Server について

MCP server implementation for Google's Gemini API

#gemini#mcp-server#google-api

Gemini MCP Server の設定

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"
      }
    }
  }
}

Gemini MCP Server のツール

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。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

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

よくある質問

1

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).

2

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.

3

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.

4

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.

5

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.

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