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MCP Gemini Server

@MCP-Mirror

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

MCP-Mirror

MCP Gemini Server について

Mirror of

#mcp-gemini#mcp-server#google-gemini

MCP Gemini 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": {
    "bsmi021_mcp-gemini-server": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@bsmi021/mcp-gemini-server",
        "--client",
        "claude"
      ]
    }
  }
}

MCP Gemini 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 MCP Gemini Server?

This server wraps the @google/genai SDK, exposing Google Gemini model capabilities as standard MCP tools. It allows other LLMs (like Cline) or MCP-compatible systems to leverage Gemini’s text generation, function calling, chat, file handling, and caching features.

How to use MCP Gemini Server?

Install via Smithery (npx -y @smithery/cli install @bsmi021/mcp-gemini-server) or manually (clone, npm install, npm run build). Configure your MCP client’s settings with the server’s path and required environment variables (GOOGLE_GEMINI_API_KEY, optionally GOOGLE_GEMINI_MODEL). Restart the client after configuration.

Key features of MCP Gemini Server

  • Standard and streaming text generation (gemini_generateContent, gemini_generateContentStream)
  • Function calling that lets Gemini request client-defined functions
  • Stateful chat sessions with history management
  • File upload, list, retrieve, and delete operations
  • Cache content creation, listing, retrieval, update, and deletion

Use cases of MCP Gemini Server

  • Integrate Gemini text generation into MCP-compatible assistants (e.g., Cline)
  • Enable function calling workflows where Gemini invokes external tools
  • Build conversational agents that maintain context across multiple turns
  • Upload and manage files (images, documents) for multimodal prompts
  • Optimize repeated prompt patterns with server-side content caching

よくある質問

1

What runtime is required?

Node.js version 18 or later.

2

Where do I get an API key?

An API key from Google AI Studio ([https://aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey)).

3

Can I use Vertex AI authentication?

No. The server only supports Google AI Studio API keys; file handling and caching features are not compatible with Vertex AI credentials.

4

How do I set a default Gemini model?

Set the `GOOGLE_GEMINI_MODEL` environment variable (e.g., `gemini-1.5-flash`). If not set, the `modelName` parameter becomes required on applicable tool calls.

5

What error types does the server return?

Errors are returned as `McpError` objects with an error code (`InvalidParams`, `InternalError`, etc.), a message, and optional details for troubleshooting (e.g., safety block reasons).

コメント

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