How to integrate Chatwork MCP with Autogen

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Introduction

This guide walks you through connecting Chatwork to AutoGen using the Composio tool router. By the end, you'll have a working Chatwork agent that can list all unread messages across rooms, upload meeting notes file to project room, get all members of marketing chat through natural language commands.

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

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

Also integrate Chatwork with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Chatwork
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Chatwork tools
  • Run a live chat loop where you ask the agent to perform Chatwork operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

The Chatwork MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Chatwork account. It provides structured and secure access to your chats, contacts, files, and rooms, so your agent can perform actions like sending messages, managing tasks, retrieving files, and organizing team communications on your behalf.

  • Room and member management: Easily fetch all chat rooms, list members in any room, and keep your workspace organized by letting your agent handle the heavy lifting.
  • Smart message retrieval and deletion: Have your agent pull recent messages from any chat, search for important info, or even delete specific messages when needed.
  • File sharing and retrieval: Seamlessly upload files to any Chatwork room or retrieve details and download links for files already shared, making document collaboration a breeze.
  • Contact and status insights: Instantly get a list of all your Chatwork contacts or check your current unread messages and task status without switching tabs.
  • Automated task and notification workflows: Let your agent monitor unread messages, mentions, and tasks, helping you stay on top of communication and never miss an important update.

Supported Tools & Triggers

Tools
Create Chatwork RoomTool to create a new group chat room in Chatwork.
Create Room Invitation LinkTool to create an invitation link for a Chatwork room.
Create Task in Chatwork RoomTool to create a new task in a Chatwork room.
Delete MessageThis tool allows you to delete a specific message from a Chatwork room by calling the DELETE endpoint at https://api.
Delete or Leave Chatwork RoomTool to leave or delete a Chatwork room.
Delete Room LinkDelete the invitation link for a Chatwork room.
Get Chatwork ContactsThis tool retrieves a list of all contacts from Chatwork.
Get Chatwork File InformationTool to get information about a specific file in a chat room.
Get Incoming Contact RequestsTool to retrieve pending contact approval requests received by the authenticated user.
Get My Chatwork ProfileTool to retrieve the authenticated user's profile information including account details, organization, contact information, and avatar URL.
Get MessageTool to retrieve information about a specific message in a Chatwork room.
Get My Chatwork StatusThis tool retrieves the current status of the authenticated user, including unread message counts and task status.
Get My Chatwork TasksTool to retrieve the authenticated user's task list from Chatwork (up to 100 items).
Get Chatwork RoomRetrieves detailed information about a specific Chatwork room using the API endpoint GET /rooms/{room_id}.
Get Room FilesTool to get list of files in a chat room (up to 100 files).
Get Room Invitation LinkRetrieves the invitation link for a specified Chatwork room using the API endpoint GET /rooms/{room_id}/link.
Get Room MembersRetrieves a complete list of all members in a specified Chatwork room using the API endpoint GET /rooms/{room_id}/members.
Get Room Messages V2Tool to retrieve messages from a Chatwork room (up to 100 messages).
Get Chatwork RoomsTool to retrieve a list of all chat rooms the authenticated user belongs to.
Get Room TasksRetrieves a list of tasks from a Chatwork room.
Get TaskRetrieves detailed information about a specific task in a Chatwork room using the API endpoint GET /rooms/{room_id}/tasks/{task_id}.
Mark Messages as ReadTool to mark messages as read in a Chatwork room.
Mark Messages as UnreadTool to mark messages as unread in a Chatwork room.
Post MessageTool to post a new message to a Chatwork room.
Update MessageTool to update an existing message in a Chatwork room.
Update Chatwork RoomTool to update chat room information (name, icon, description).
Update Room Invitation LinkTool to update the invitation link settings for a Chatwork room.
Update Room MembersUpdates the complete member list of a Chatwork room with bulk assignment of member roles (admin, member, readonly).
Update Task StatusTool to update the completion status of a task in a Chatwork room.
Upload File to Chatwork RoomThis tool allows users to upload files to a specific Chatwork room.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Chatwork account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Chatwork via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Chatwork connections to use

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Chatwork session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["chatwork"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Chatwork tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Chatwork assistant agent with MCP tools
    agent = AssistantAgent(
        name="chatwork_assistant",
        description="An AI assistant that helps with Chatwork operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Chatwork tools from the workbench

Run the interactive chat loop

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

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Chatwork tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Chatwork and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Chatwork session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["chatwork"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Chatwork assistant agent with MCP tools
        agent = AssistantAgent(
            name="chatwork_assistant",
            description="An AI assistant that helps with Chatwork operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Chatwork through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Chatwork, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

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FAQ

What are the differences in Tool Router MCP and Chatwork MCP?

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

Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen 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 Chatwork tools.

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

Yes, absolutely. You can configure which Chatwork 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 Chatwork data and credentials are handled as safely as possible.

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