How to integrate Microsoft teams MCP with LangChain

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Introduction

This guide walks you through connecting Microsoft teams to LangChain using the Composio tool router. By the end, you'll have a working Microsoft teams agent that can add new member to project team, schedule an online meeting for sales, list all chats i’m part of, get details for marketing team channel through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Microsoft teams account through Composio's Microsoft teams MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Microsoft teams project to Composio
  • Create a Tool Router MCP session for Microsoft teams
  • Initialize an MCP client and retrieve Microsoft teams tools
  • Build a LangChain agent that can interact with Microsoft teams
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Microsoft teams MCP server, and what's possible with it?

The Microsoft Teams MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Microsoft Teams account. It provides structured and secure access to your Teams workspace, so your agent can perform actions like managing chats, sending messages, creating meetings, and organizing teams on your behalf.

  • Automated chat and message management: Let your agent retrieve, read, and summarize messages from any Teams chat, or fetch all chats you’re part of for quick updates.
  • Team and channel organization: Easily create new teams, add members, get channel details, or archive and delete teams to keep your workspace organized.
  • Scheduling online meetings: Have your agent schedule standalone Teams meetings instantly, making it simple to coordinate with colleagues or clients without manual setup.
  • Granular access to team and chat details: Fetch full information about specific teams, channels, or even individual messages with precision, enabling rich contextual workflows.
  • Seamless membership and collaboration management: Add or update members in teams with a prompt, ensuring the right people always have access to the conversations and resources they need.

Supported Tools & Triggers

Tools
Add member to teamTool to add a user to a microsoft teams team.
Archive Teams teamTool to archive a microsoft teams team.
Get all chatsRetrieves all microsoft teams chats a specified user is part of, supporting filtering, property selection, and pagination.
Get all chat messagesRetrieves all messages from a specified microsoft teams chat using the microsoft graph api, automatically handling pagination; ensure `chat id` is valid and odata expressions in `filter` or `select` are correct.
Create online meetingUse to schedule a new standalone microsoft teams online meeting, i.
Create TeamTool to create a new microsoft teams team.
Delete Teams teamTool to delete a microsoft teams team.
Get team channelTool to get a specific channel in a team.
Get chat messageTool to get a specific chat message.
Get TeamTool to get a specific team.
List message repliesTool to list replies to a channel message.
List team membersTool to list members of a microsoft teams team.
List Teams templatesTool to list available microsoft teams templates.
List usersTool to list all users in the organization.
Create a channelCreates a new 'standard', 'private', or 'shared' channel within a specified microsoft teams team.
Create ChatCreates a new chat; if a 'oneonone' chat with the specified members already exists, its details are returned, while 'group' chats are always newly created.
Get Teams messageRetrieves a specific message from a microsoft teams channel using its team, channel, and message ids.
List TeamsRetrieves microsoft teams accessible by the authenticated user, allowing filtering, property selection, and pagination.
List team channelsRetrieves channels for a specified microsoft teams team id (must be valid and for an existing team), with options to include shared channels, filter results, and select properties.
List chat messagesRetrieves messages (newest first) from an existing and accessible microsoft teams one-on-one chat, group chat, or channel thread, specified by `chat id`.
List PeopleRetrieves a list of people relevant to a specified user from microsoft graph, noting the `search` parameter is only effective if `user id` is 'me'.
Post message to Teams channelPosts a new text or html message to a specified channel in a microsoft teams team.
Send message to Teams chatSends a non-empty message (text or html) to a specified, existing microsoft teams chat; content must be valid html if `content type` is 'html'.
Reply to Teams channel messageSends a reply to an existing message, identified by `message id`, within a specific `channel id` of a given `team id` in microsoft teams.
Unarchive Teams teamTool to unarchive a microsoft teams team.
Update Teams channel messageTool to update a message in a channel.
Update Teams chat messageTool to update a specific message in a chat.
Update TeamTool to update the properties of a team.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Microsoft teams functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Microsoft teams tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Microsoft teams
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['microsoft_teams']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Microsoft teams tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Microsoft teams tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "microsoft_teams-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Microsoft teams MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Microsoft teams tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Microsoft teams related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Microsoft teams and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['microsoft_teams']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "microsoft_teams-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Microsoft teams related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Microsoft teams through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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FAQ

What are the differences in Tool Router MCP and Microsoft teams MCP?

With a standalone Microsoft teams MCP server, the agents and LLMs can only access a fixed set of Microsoft teams tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Microsoft teams and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Microsoft teams tools.

Can I manage the permissions and scopes for Microsoft teams while using Tool Router?

Yes, absolutely. You can configure which Microsoft teams scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Microsoft teams data and credentials are handled as safely as possible.

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