Google Workspace MCP Server

Overview

The Google Workspace MCP Server bridges AI assistants and Google’s core productivity services—Gmail and Calendar—through the Model Context Protocol. By exposing a curated set of tools that mirror the most common Gmail and Calendar actions, it eliminates the need for developers to write custom API wrappers or manage OAuth flows manually. Instead, an AI assistant can issue a simple tool call such as or , and the server translates that into a secure, authenticated request to Google’s REST APIs.

This server solves two key pain points for AI‑driven workflows. First, it handles the OAuth 2.0 dance once, storing a refresh token that keeps the assistant authorized without user intervention during each session. Second, it abstracts the complexity of Gmail query syntax and Calendar event formatting behind a clean, JSON‑based interface. Developers can therefore focus on building conversational logic or business rules rather than boilerplate authentication and data mapping.

Key capabilities include:

• Email Management: List, search, send, and modify emails with support for advanced Gmail query language, CC/BCC handling, and label manipulation (archive, trash, read/unread).
• Calendar Operations: Retrieve upcoming events with date filtering, create new events (including attendee invitations), update existing entries, and delete events.
• Security & Permissions: The server requests the minimal scopes required (, , and ) and can be configured to run under a service account or user credentials.

Real‑world use cases abound: an AI personal assistant can fetch unread messages, draft replies, and schedule follow‑up meetings; a project management bot can pull task reminders from the calendar and send status updates via email; an enterprise chatbot can archive completed tickets directly in Gmail while updating a shared calendar. In each scenario, the MCP server acts as a trusted intermediary that guarantees consistent authentication and rate‑limit handling.

Integrating this server into an AI workflow is straightforward. Once the MCP configuration is added to the client’s settings, the assistant can invoke any of the exposed tools by name, passing a JSON payload. The server processes the call, communicates with Google’s APIs, and returns structured results that the assistant can embed in its responses or use to trigger further actions. This tight coupling between conversational context and external data sources empowers developers to create richer, more responsive AI experiences without reinventing the wheel.