How to integrate Miro MCP with Autogen

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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

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 & Triggers

Tools
Attach Tag To ItemTool to attach an existing tag to a specific item on a Miro board.
Create App Card ItemTool to add an app card item to a board.
Create BoardTool to create a new board.
Create Card ItemTool to create a card item on a Miro board.
Create ConnectorTool to create a connector (edge/arrow) that links two existing board items.
Create Document ItemTool to create a document item on a Miro board by providing a URL to the document.
Create Document Item Using File From DeviceTool to create a document item on a Miro board using a URL to the document.
Create Embed ItemTool to create an embed item on a Miro board by providing a URL to embed content (YouTube videos, websites, etc.
Create Frame ItemTool to add a frame item to a Miro board.
Create GroupTool to create a group on a Miro board by grouping multiple items together.
Create Image Item Using Local FileTool to create an image item on a Miro board by uploading a local image file.
Create Items in BulkTool to create multiple items on a Miro board in a single request.
Create Mind Map Node (Experimental)Tool to create a mind map node on a Miro board.
Create Shape ItemTool to create a shape item on a Miro board.
Create Sticky Note ItemTool to create a sticky note item on a Miro board.
Create TagTool to create a new tag on a Miro board.
Create Text ItemTool to create a text item on a Miro board.
Delete App Card ItemTool to delete an app card item from a board.
Delete Card ItemTool to delete a card item from a board.
Delete ConnectorTool to delete a specific connector from a board.
Delete Document ItemTool to delete a document item from a board.
Delete Embed ItemTool to delete an embed item from a board.
Delete Frame ItemTool to delete a frame item from a Miro board.
Delete GroupTool to delete a group from a board.
Delete Image ItemTool to delete an image item from a board.
Delete ItemTool to delete a specific item from a board.
Delete Mind Map Node (Experimental)Tool to delete a mind map node from a board.
Delete Shape ItemTool to delete a shape item from a board.
Delete Sticky Note ItemTool to delete a sticky note item from a board.
Delete TagTool to delete a specific tag from a board.
Delete Text ItemTool to delete a text item from a board.
Get All GroupsTool to retrieve all groups on a Miro board with cursor-based pagination.
Get App Card Item 2Tool to retrieve a specific app card item by its ID from a Miro board.
Get Board ItemsTool to list items on a Miro board (shapes, stickies, cards, etc.
Get Board MembersTool to retrieve a list of members for a board.
Get Boards V2Tool to retrieve accessible boards with optional filters.
Get Card ItemTool to retrieve a specific card item from a Miro board.
Get ConnectorTool to retrieve a specific connector by its ID.
Get ConnectorsTool to retrieve a list of connectors on a board.
Get Document ItemTool to retrieve a specific document item from a Miro board by its ID.
Get Embed ItemTool to retrieve a specific embed item from a board by its ID.
Get Frame ItemTool to retrieve a specific frame item from a Miro board.
Get Group By IDTool to retrieve a specific group by its ID.
Get Image ItemTool to retrieve a specific image item from a board.
Get Item TagsTool to retrieve tags attached to a specific item on a Miro board.
Get Mind Map NodeTool to retrieve a specific mind map node from a board.
Get Mind Map Nodes (Experimental)Tool to retrieve mind map nodes from a Miro board.
Get oEmbed DataTool to retrieve oEmbed data for a Miro board.
Get Shape ItemTool to retrieve a specific shape item from a Miro board by its ID.
Get Specific BoardTool to retrieve detailed information about a specific board by its ID.
Get Specific Board MemberTool to retrieve details of a specific board member.
Get Specific ItemTool to retrieve a specific item from a Miro board by its ID.
Get Sticky Note ItemTool to retrieve a specific sticky note item from a board by its ID.
Get TagTool to retrieve details of a specific tag on a board.
Get Text ItemTool to retrieve a specific text item from a Miro board by its ID.
List Board TagsTool to list all tags on a Miro board.
Get Organization ContextRetrieves the organization associated with the current access token.
Share BoardTool to share a board by inviting users via email.
Update App Card Item 2Tool to update an app card item on a Miro board.
Update BoardTool to update properties of a specific board.
Update Board MemberTool to update the role of a specific board member.
Update Card ItemTool to update a card item on a Miro board.
Update ConnectorTool to update an existing connector on a Miro board.
Update Document ItemTool to update a document item on a Miro board.
Update Embed ItemTool to update an embed item on a board.
Update Frame ItemTool to update a frame item on a Miro board.
Update GroupTool to update a group on a Miro board with new items.
Update Image ItemTool to update an existing image item on a board.
Update Item Position or ParentTool to update an item's position or parent frame on a Miro board.
Update Shape ItemTool to update an existing shape item on a Miro board.
Update Sticky Note ItemTool to update a sticky note item on a Miro board.
Update TagTool to update a tag on a board.
Update Text ItemTool to update a text item on a Miro board.

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 Miro 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 Miro 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 Miro 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 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
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

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 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
    )

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

Run the interactive chat 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")
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

Complete Code

Here's the complete code to get you started with Miro 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 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

FAQ

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.

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