MCP Teams Server

The MCP Teams Server bridges the gap between conversational AI assistants and Microsoft Teams, enabling seamless interaction with channel conversations without leaving the chat interface. By exposing Teams as an MCP endpoint, developers can give AI agents the ability to read existing messages, start new discussion threads, reply to posts, and even mention specific users—all through the same request/response model that powers tools like Claude. This eliminates the need for separate SDKs or custom integrations, allowing a single AI workflow to orchestrate communications across Teams and other services.

At its core, the server implements four key capabilities: read, write, reply, and mention. A client can request the full history of a channel, retrieve all members of a team, or fetch the replies to a particular thread. Conversely, it can create a new thread with a title and content, automatically mention selected users, update an existing thread by adding replies that include mentions, or simply post a message to a channel. These operations are exposed through well‑defined MCP resources and actions, making it trivial for an AI assistant to compose a message, decide whom to tag, and push the result back into Teams as part of its conversational flow.

The server’s design prioritizes security and scalability. It requires Azure AD credentials—app ID, password, tenant ID—and supports both single‑tenant and multi‑tenant deployments. By running as a lightweight Python service (or via the provided Docker image), it can be hosted in any cloud environment, from on‑premises servers to serverless containers. This flexibility means teams can embed Teams interactions into existing AI pipelines—such as automating meeting summaries, coordinating project updates, or managing support tickets—without exposing sensitive credentials in the client code.

Typical use cases include:

• Automated stand‑up reports – an AI agent reads the previous day’s notes, compiles a concise summary, and posts it to the channel, tagging relevant stakeholders.
• Dynamic task delegation – when a project milestone is reached, the assistant creates a new thread, assigns tasks to team members via mentions, and tracks progress through replies.
• Real‑time support – a chatbot can listen to incoming channel messages, interpret user queries, and respond directly in the thread, ensuring context is preserved.

By treating Teams as a first‑class MCP resource, developers can build end‑to‑end AI experiences that feel native to their organization’s collaboration platform. The server’s clear API surface, combined with robust authentication and a Docker‑ready deployment model, makes it an attractive choice for teams looking to extend their AI assistants into the everyday workflows of Microsoft Teams.