What are the prerequisites for using Gmail MCP Server?
Python 3.6+, Gmail API credentials from Google Cloud Console, a Gemini API key from Google AI Studio, and the packages listed in `requirements.txt`.
@gkdivya
基本情報
概要はまだありません
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。
Gmail MCP Server is a FastMCP-based integration that exposes Gmail operations as tools for AI assistants. It uses Google OAuth 2.0 and the Gmail API to send, read, manage, and trash emails. The project also includes a smart client that leverages Google’s Generative AI (Gemini) to interpret natural language requests.
Install dependencies from requirements.txt, place Gmail API OAuth credentials as credentials.json, and set the Gemini API key in a .env file. Start the server with python gmail_server.py --creds credentials.json --token token.json. Then run the smart client with python test_gmail_server.py to interact using natural language commands.
send_email, get_unread_emails, read_email, trash_email, open_email, mark_email_as_readPython 3.6+, Gmail API credentials from Google Cloud Console, a Gemini API key from Google AI Studio, and the packages listed in `requirements.txt`.
The server uses OAuth 2.0. On first run, it opens a browser for you to log in to Google and grant permissions, then saves a `token.json` file for subsequent requests.
`credentials.json` (OAuth client ID), `token.json` (generated token), and `.env` (Gemini API key) reside in the project root. The `.gitignore` prevents accidental commits of these sensitive files.
Add the tool implementation in `gmail_server.py`. The smart client automatically discovers and exposes new tools, and the LLM learns to handle requests from the tool descriptions.
Yes. The `test_gmail_server.py` client uses Gemini to interpret plain‑English requests (e.g., “Send an email to Alice”) and converts them into the appropriate tool calls, asking for missing information when needed.
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