What is the purpose of this server?
It allows Claude to create and interact with multiple Gemini AI agents during a conversation, each with its own role and context, and hand off information between them.
@ivangrynenko
基本情報
MCP server for orchestrating multiple Gemini AI agents with persistent sessions
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-mcp-ivangrynenko": {
"command": "python",
"args": [
"-m",
"gemini_mcp"
]
}
}
}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。
An MCP server that enables Claude to orchestrate multiple Gemini AI agents dynamically, with each agent maintaining its own conversational context through persistent sessions. It is designed for developers using Claude Desktop to create, manage, and hand off context between specialized AI agents in real time.
Install with pip install -r requirements.txt, set your GEMINI_API_KEY in a .env file, and add the server configuration to Claude Desktop’s MCP settings under the name gemini-orchestrator. Run the server with python -m gemini_mcp and then invoke tools like ai_agents_create, ai_agents_send_message, and ai_agents_handoff from Claude.
It allows Claude to create and interact with multiple Gemini AI agents during a conversation, each with its own role and context, and hand off information between them.
Clone the repository, install dependencies, create a `.env` file with your Gemini API key, and add a `gemini-orchestrator` entry to Claude Desktop’s MCP configuration JSON.
Two options: in‑memory (fast, data lost on restart) for development/testing, and SQLite (persistent, slightly slower) for production.
Yes, use the `ai_agents_list` tool to get a list of all agents with their roles, session IDs, creation timestamps, and message counts.
Ensure you have set the environment variable in your `.env` file and that the file is correctly loaded. Also verify the key is valid and that you’re using the correct casing.
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