Gmail MCP Server

The Gmail Model Context Protocol (MCP) server bridges the gap between AI assistants and personal email workflows. It exposes a concise set of tools that let an assistant read, send, delete, and organize Gmail messages while preserving user control through explicit prompts. This design aligns with privacy best practices: the assistant can only perform actions after a user explicitly confirms each request, ensuring that sensitive communications remain under human supervision.

At its core, the server provides six primary tools. send-email delivers messages to any address, capturing status and a unique message ID for tracking. trash-email moves selected messages to the trash, while mark-email-as-read updates read flags without retrieving content. For inbox management, get-unread-emails lists all unread messages and read-email fetches the full content of a chosen email, automatically marking it as read. Finally, open-email launches the message in the default web browser, offering a quick visual review. Each tool is intentionally lightweight, returning only essential metadata and success confirmations to keep the conversation flow smooth.

Developers can integrate this server into existing MCP workflows by configuring a simple command that launches the Gmail client with OAuth credentials and token storage paths. Once running, any MCP-enabled assistant—such as Claude Desktop—can query the server’s tool registry and invoke email operations. The authentication flow, which opens a browser for user consent, is handled automatically; tokens are cached in a secure local file for future sessions.

Real‑world use cases abound. A project manager could ask an assistant to draft a status email to stakeholders, have the assistant compose and send it after confirmation, and then retrieve the message ID for audit purposes. A support agent might request unread customer inquiries, read them through the assistant’s interface, and then archive or delete resolved tickets. In research settings, an AI can help curate newsletters by pulling unread academic papers and forwarding them to collaborators.

What sets this MCP server apart is its blend of simplicity, security, and user‑centric design. By limiting actions to confirmed prompts and exposing only essential email operations, it offers developers a trustworthy toolset that integrates seamlessly into conversational AI pipelines without compromising privacy or requiring complex configuration.