# How to integrate Miro MCP with Autogen ```json { "title": "How to integrate Miro MCP with Autogen", "toolkit": "Miro", "toolkit_slug": "miro", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/miro/framework/autogen", "markdown_url": "https://composio.dev/toolkits/miro/framework/autogen.md", "updated_at": "2026-05-06T08:20:18.530Z" } ``` ## Introduction This guide walks you through connecting Miro to AutoGen using the Composio tool router. By the end, you'll have a working Miro agent that can create a new board for marketing brainstorm, list all boards owned by your team, show members of the q2 planning board through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Miro account through Composio's Miro MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Miro with - [ChatGPT](https://composio.dev/toolkits/miro/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/miro/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/miro/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/miro/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/miro/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/miro/framework/codex) - [Cursor](https://composio.dev/toolkits/miro/framework/cursor) - [VS Code](https://composio.dev/toolkits/miro/framework/vscode) - [OpenCode](https://composio.dev/toolkits/miro/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/miro/framework/openclaw) - [Hermes](https://composio.dev/toolkits/miro/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/miro/framework/cli) - [Google ADK](https://composio.dev/toolkits/miro/framework/google-adk) - [LangChain](https://composio.dev/toolkits/miro/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/miro/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/miro/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/miro/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/miro/framework/crew-ai) ## 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 Miro - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Miro tools - Run a live chat loop where you ask the agent to perform Miro 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 Miro MCP server, and what's possible with it? The Miro MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Miro account. It provides structured and secure access to your whiteboards, so your agent can create new boards, manage board content, organize workflows, and collaborate visually—all on your behalf. - Automated board creation and setup: Instantly instruct your agent to create new Miro boards with specific names and descriptions for projects, brainstorming, or workshops. - Visual content management: Ask your agent to add, retrieve, or delete items such as shapes, sticky notes, app cards, or document items from any board, keeping your workspace tidy and up to date. - Efficient team and member management: Have your agent fetch and list all members of a board so you can easily track collaborators and manage access. - Seamless board organization and retrieval: Let your agent search and retrieve boards by team, owner, or keyword to keep your workspace organized and easy to navigate. - Connector and tag insights: Direct your agent to get details on connectors and tags used within boards, helping you map relationships and categorize content visually. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `MIRO_CREATE_BOARD` | Create Board | Tool to create a new board. use when you need to set up a board with a specific name, description, and policies. example: "create a new board named project plan". | | `MIRO_DELETE_APP_CARD_ITEM` | Delete App Card Item | Tool to delete an app card item from a board. use when you need to remove an app card item created by your app after it is no longer needed. | | `MIRO_DELETE_DOCUMENT_ITEM` | Delete Document Item | Tool to delete a document item from a board. use when you need to remove a document item (e.g., pdf or image) that is no longer relevant. example: "delete the document item with id 'item456' from board 'board123'." | | `MIRO_DELETE_ITEM` | Delete Item | Tool to delete a specific item from a board. use when you need to remove an item (e.g., shape, sticky note) after confirming its board and item ids. | | `MIRO_GET_APP_CARD_ITEM` | Get App Card Item | Tool to retrieve a specific app card item by its id. use when you need the details of an existing app card item. | | `MIRO_GET_BOARD` | Get Board | Tool to retrieve details of a specific board. use when you have a board id and need to fetch its metadata. | | `MIRO_GET_BOARD_MEMBERS` | Get Board Members | Tool to retrieve a list of members for a board. use when you need to list all users with access to a board after confirming its id. | | `MIRO_GET_BOARDS` | Get Boards | Tool to retrieve accessible boards with optional filters. use when you need to list or search boards by team, project, owner, or keywords. | | `MIRO_GET_CONNECTORS` | Get Connectors | Tool to retrieve a list of connectors on a board. use after confirming the board id and when you need to page through connector items. | | `MIRO_GET_TAG` | Get Tag | Tool to retrieve details of a specific tag on a board. use when you have a board id and tag id and need its metadata. | | `MIRO_LIST_ORGANIZATIONS` | List Organizations | Tool to retrieve list of organizations accessible to the user. use when you need to view all available organizations. | | `MIRO_MIRO_CREATE_APP_CARD_ITEM` | Create App Card Item | Tool to add an app card item to a board. use when you need to push a rich preview card with custom fields into a miro board (e.g., after assembling card data). | | `MIRO_UPDATE_APP_CARD_ITEM` | Update App Card Item | Tool to update an app card item on a board. use when you need to modify properties of an existing app card item. include only fields to change. | | `MIRO_UPDATE_BOARD` | Update Board | Tool to update properties of a specific board. use when you have a board id and need to modify its name, description, or permissions policy. use after confirming the board exists. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Miro MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Miro. Instead of manually wiring Miro APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. 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 You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Miro account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 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 Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Miro via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables 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 Miro connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Miro tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```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 Miro session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["miro"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen 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 ```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")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Miro tools from the workbench ```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 Miro assistant agent with MCP tools agent = AssistantAgent( name="miro_assistant", description="An AI assistant that helps with Miro operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Miro 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 ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Miro 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") ``` ## Complete Code ```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 Miro session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["miro"] ) 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 Miro assistant agent with MCP tools agent = AssistantAgent( name="miro_assistant", description="An AI assistant that helps with Miro 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 Miro 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 Miro 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 Miro, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Miro MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/miro/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/miro/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/miro/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/miro/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/miro/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/miro/framework/codex) - [Cursor](https://composio.dev/toolkits/miro/framework/cursor) - [VS Code](https://composio.dev/toolkits/miro/framework/vscode) - [OpenCode](https://composio.dev/toolkits/miro/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/miro/framework/openclaw) - [Hermes](https://composio.dev/toolkits/miro/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/miro/framework/cli) - [Google ADK](https://composio.dev/toolkits/miro/framework/google-adk) - [LangChain](https://composio.dev/toolkits/miro/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/miro/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/miro/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/miro/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/miro/framework/crew-ai) ## Related Toolkits - [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing. - [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams. - [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content. - [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates. - [Canva](https://composio.dev/toolkits/canva) - Canva is a drag-and-drop design suite for creating professional graphics, presentations, and marketing materials. It makes it easy for anyone to design with beautiful templates and a vast library of elements. - [Claid ai](https://composio.dev/toolkits/claid_ai) - Claid.ai delivers AI-driven image editing APIs for tasks like background removal, upscaling, and color correction. It helps automate and enhance image workflows with powerful, developer-friendly tools. - [Cloudinary](https://composio.dev/toolkits/cloudinary) - Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally. - [Cults](https://composio.dev/toolkits/cults) - Cults is a digital marketplace for 3D printing models, connecting designers and makers. It lets creators share, sell, and discover a huge variety of printable designs easily. - [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks. - [Dreamstudio](https://composio.dev/toolkits/dreamstudio) - DreamStudio is Stability AI’s platform for generating and editing images with AI. It lets you easily turn ideas into stunning visuals, fast. - [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools. - [Html to image](https://composio.dev/toolkits/html_to_image) - Html to image converts HTML and CSS into images or captures web page screenshots. Instantly generate visuals from code or web content—no manual screenshots needed. - [Imagior](https://composio.dev/toolkits/imagior) - Imagior is an AI-powered image generation platform that lets you create and customize images using dynamic templates and APIs. Perfect for businesses and creators needing fast, scalable visuals without design hassle. - [Imejis io](https://composio.dev/toolkits/imejis_io) - Imejis io is an API-based image generation platform with powerful customization and template support. It lets you create and modify images in seconds, no manual design work required. - [Imgix](https://composio.dev/toolkits/imgix) - Imgix is a real-time image processing and delivery service for developers. It helps you optimize, transform, and deliver images efficiently at any scale. - [Kraken io](https://composio.dev/toolkits/kraken_io) - Kraken.io is an image optimization and compression platform. It helps you shrink image file sizes while keeping visual quality intact. - [Logo dev](https://composio.dev/toolkits/logo_dev) - Logo.dev is an API and database for high-resolution company logos and brand metadata. Instantly fetch official logos from any domain without scraping or manual searching. - [Mural](https://composio.dev/toolkits/mural) - Mural is a digital whiteboard platform for distributed visual collaboration. It helps teams brainstorm, map ideas, and diagram together in real time. - [Pexels](https://composio.dev/toolkits/pexels) - Pexels is a free stock library offering high-quality photos and videos via API. Instantly boost your app or website with stunning visuals for any use case. - [Placid](https://composio.dev/toolkits/placid) - Placid is a creative automation toolkit that generates images, PDFs, and videos from custom templates via API. Effortlessly automate creative workflows and dynamic content creation at scale. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Miro MCP? With a standalone Miro MCP server, the agents and LLMs can only access a fixed set of Miro tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Miro 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 Miro tools. ### Can I manage the permissions and scopes for Miro while using Tool Router? Yes, absolutely. You can configure which Miro 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 Miro data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)