How to integrate Miro MCP with LlamaIndex

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Introduction

This guide walks you through connecting Miro to LlamaIndex 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 LlamaIndex 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:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Miro
  • Connect LlamaIndex to the Miro MCP server
  • Build a Miro-powered agent using LlamaIndex
  • Interact with Miro through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built 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 you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Miro account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Miro

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Miro access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called miro_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["miro"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Miro actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Miro actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, miro)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Miro tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Miro database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Miro

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Miro, then start asking questions.

Complete Code

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

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["miro"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Miro actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Miro actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Miro to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Miro tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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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 LlamaIndex?

Yes, you can. LlamaIndex 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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