# How to integrate Chatwork MCP with Pydantic AI ```json { "title": "How to integrate Chatwork MCP with Pydantic AI", "toolkit": "Chatwork", "toolkit_slug": "chatwork", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/chatwork/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/chatwork/framework/pydantic-ai.md", "updated_at": "2026-05-06T08:05:36.480Z" } ``` ## Introduction This guide walks you through connecting Chatwork to Pydantic AI 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 Pydantic AI 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 - [OpenAI Agents SDK](https://composio.dev/toolkits/chatwork/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/chatwork/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/chatwork/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/chatwork/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/chatwork/framework/codex) - [OpenClaw](https://composio.dev/toolkits/chatwork/framework/openclaw) - [Hermes](https://composio.dev/toolkits/chatwork/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/chatwork/framework/cli) - [Google ADK](https://composio.dev/toolkits/chatwork/framework/google-adk) - [LangChain](https://composio.dev/toolkits/chatwork/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/chatwork/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/chatwork/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/chatwork/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/chatwork/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Chatwork - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Chatwork workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## 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 | Tool slug | Name | Description | |---|---|---| | `CHATWORK_CHATWORK_DELETE_MESSAGE` | Delete Message | This tool allows you to delete a specific message from a chatwork room by calling the delete endpoint at https://api.chatwork.com/v2/rooms/{room id}/messages/{message id}. it requires authentication using a chatwork api token provided in the x-chatworktoken header, and the necessary permissions to delete messages in the specified room. | | `CHATWORK_GET_CHATWORK_CONTACTS` | Get Chatwork Contacts | This tool retrieves a list of all contacts from chatwork. it is a fundamental tool that fetches all contact information such as account id, room id, name, chatwork id, organization details, department, and avatar image url, without needing additional parameters beyond authentication. | | `CHATWORK_GET_FILE` | Get Chatwork File | This tool retrieves information about a specific file in a chat room. the api endpoint get /v2/rooms/{room id}/files/{file id} provides file details such as file id, account id, message id, filename, filesize, upload time, and download url, which are useful for retrieving file metadata, verifying file existence, and managing file sharing within chatwork. | | `CHATWORK_GET_MY_STATUS` | Get My Chatwork Status | This tool retrieves the current status of the authenticated user, including unread message counts and task status. it provides a quick overview of unread messages, mentions, and tasks, making it valuable for monitoring chatwork activity and building automation workflows. | | `CHATWORK_GET_ROOM_MEMBERS` | Get Room Members | This tool retrieves a list of all members in a specified chatwork room using the endpoint get /rooms/{room id}/members. it provides essential details like account id, role, name, chatwork id, organization id, and organization name, complementing the existing suite of room management tools. | | `CHATWORK_GET_ROOM_MESSAGES` | Get Room Messages | This tool retrieves messages from a specific chatwork room using the get https://api.chatwork.com/v2/rooms/{room id}/messages endpoint. it requires a room id parameter and an optional force flag to refresh the cache by retrieving the 100 newest messages. | | `CHATWORK_GET_ROOMS` | Get Chatwork Rooms | This tool retrieves a list of all chat rooms associated with the authenticated chatwork account. it includes group chats, direct chats, and personal chats, and does not require any additional parameters beyond authentication. | | `CHATWORK_UPLOAD_FILE` | Upload File to Chatwork Room | This tool allows users to upload files to a specific chatwork room. it enables file sharing functionality within the chatwork platform by providing an endpoint to upload files (along with an optional message) to a given room. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Chatwork MCP server is an implementation of the Model Context Protocol that connects your AI agent to Chatwork. It provides structured and secure access so your agent can perform Chatwork operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before starting, make sure you have: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Chatwork - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Chatwork - MCPServerStreamableHTTP connects to the Chatwork MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Chatwork 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 ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Chatwork composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["chatwork"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Chatwork endpoint - The agent uses GPT-5 to interpret user commands and perform Chatwork operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent chatwork_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[chatwork_mcp], instructions=( "You are a Chatwork assistant. Use Chatwork tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Chatwork API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Chatwork.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Chatwork composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["chatwork"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent chatwork_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[chatwork_mcp], instructions=( "You are a Chatwork assistant. Use Chatwork tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Chatwork.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Chatwork through Composio's Tool Router. With this setup, your agent can perform real Chatwork actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Chatwork for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## How to build Chatwork MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/chatwork/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/chatwork/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/chatwork/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/chatwork/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/chatwork/framework/codex) - [OpenClaw](https://composio.dev/toolkits/chatwork/framework/openclaw) - [Hermes](https://composio.dev/toolkits/chatwork/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/chatwork/framework/cli) - [Google ADK](https://composio.dev/toolkits/chatwork/framework/google-adk) - [LangChain](https://composio.dev/toolkits/chatwork/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/chatwork/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/chatwork/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/chatwork/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/chatwork/framework/crew-ai) ## Related Toolkits - [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable. - [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management. - [Slack](https://composio.dev/toolkits/slack) - Slack is a channel-based messaging platform for teams and organizations. It helps people collaborate in real time, share files, and connect all their tools in one place. - [Gong](https://composio.dev/toolkits/gong) - Gong is a platform for video meetings, call recording, and team collaboration. It helps teams capture conversations, analyze calls, and turn insights into action. - [Microsoft teams](https://composio.dev/toolkits/microsoft_teams) - Microsoft Teams is a collaboration platform that combines chat, meetings, and file sharing within Microsoft 365. It keeps distributed teams connected and productive through seamless virtual communication. - [Slackbot](https://composio.dev/toolkits/slackbot) - Slackbot is a conversational automation tool for Slack that handles reminders, notifications, and automated responses. It boosts team productivity by streamlining onboarding, answering FAQs, and managing timely alerts—all right inside Slack. - [2chat](https://composio.dev/toolkits/_2chat) - 2chat is an API platform for WhatsApp and multichannel text messaging. It streamlines chat automation, group management, and real-time messaging for developers. - [Agent mail](https://composio.dev/toolkits/agent_mail) - Agent mail provides AI agents with dedicated email inboxes for sending, receiving, and managing emails. It empowers agents to communicate autonomously with people, services, and other agents—no human intervention needed. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Clickmeeting](https://composio.dev/toolkits/clickmeeting) - ClickMeeting is a cloud-based platform for running online meetings and webinars. It helps businesses and individuals host, manage, and engage virtual audiences with ease. - [Confluence](https://composio.dev/toolkits/confluence) - Confluence is Atlassian's team collaboration and knowledge management platform. It helps your team organize, share, and update documents and project content in one secure workspace. - [Dailybot](https://composio.dev/toolkits/dailybot) - DailyBot streamlines team collaboration with chat-based standups, reminders, and polls. It keeps work flowing smoothly in your favorite messaging platforms. - [Dialmycalls](https://composio.dev/toolkits/dialmycalls) - Dialmycalls is a mass notification service for sending voice and text messages to contacts. It helps teams and organizations quickly broadcast urgent alerts and updates. - [Dialpad](https://composio.dev/toolkits/dialpad) - Dialpad is a cloud-based business phone and contact center system for teams. It unifies voice, video, messaging, and meetings across your devices. - [Discord](https://composio.dev/toolkits/discord) - Discord is a real-time messaging and VoIP platform for communities and teams. It lets users chat, share media, and collaborate across public and private channels. - [Discordbot](https://composio.dev/toolkits/discordbot) - Discordbot is an automation tool for Discord servers that handles moderation, messaging, and user engagement. It helps communities run smoothly by automating routine and complex tasks. - [Echtpost](https://composio.dev/toolkits/echtpost) - Echtpost is a secure digital communication platform for encrypted document and message exchange. It ensures confidential data stays private and protected during transmission. - [Egnyte](https://composio.dev/toolkits/egnyte) - Egnyte is a cloud-based platform for secure file sharing, storage, and governance. It helps teams collaborate efficiently while maintaining data compliance and security. - [Google Meet](https://composio.dev/toolkits/googlemeet) - Google Meet is a secure video conferencing platform for virtual meetings, chat, and screen sharing. It helps teams connect, collaborate, and communicate seamlessly from anywhere. - [Heartbeat](https://composio.dev/toolkits/heartbeat) - Heartbeat is a plug-and-play platform for building and managing online communities. It helps you organize users, channels, events, and discussions in one place. ## Frequently Asked Questions ### 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 Pydantic AI? Yes, you can. Pydantic AI 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)