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
Basic information
No overview available yet
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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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