How to integrate Miro MCP with Pydantic AI

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Introduction

This guide walks you through connecting Miro to Pydantic AI 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 Pydantic AI 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Miro
  • 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 Miro 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 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

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

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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Miro
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

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

Import dependencies

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()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Miro
  • MCPServerStreamableHTTP connects to the Miro MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

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 Miro
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["miro"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Miro 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

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
miro_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[miro_mcp],
    instructions=(
        "You are a Miro assistant. Use Miro tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Miro endpoint
  • The agent uses GPT-5 to interpret user commands and perform Miro operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

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 Miro.\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()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Miro API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Miro and Pydantic AI:

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 Miro
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["miro"],
    )
    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
    miro_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[miro_mcp],
        instructions=(
            "You are a Miro assistant. Use Miro 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 Miro.\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 Miro through Composio's Tool Router. With this setup, your agent can perform real Miro 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 + Miro for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

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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 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 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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