How to integrate Figma MCP with Autogen

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Introduction

This guide walks you through connecting Figma to AutoGen using the Composio tool router. By the end, you'll have a working Figma agent that can add a comment to this figma file, convert design tokens to tailwind css, delete a reaction from a comment, create a webhook for figma team events through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Figma account through Composio's Figma MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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 Figma
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Figma tools
  • Run a live chat loop where you ask the agent to perform Figma 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 Figma MCP server, and what's possible with it?

The Figma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Figma account. It provides structured and secure access to your Figma workspace, so your agent can perform actions like commenting on designs, managing design tokens, linking developer resources, and automating collaboration workflows on your behalf.

  • Automated commenting and feedback loops: Have your agent add, reply to, or delete comments on Figma files and branches to streamline design reviews and team discussions.
  • Design token management and conversion: Let the agent extract, update, or convert design tokens in your files, including generating Tailwind CSS configurations for seamless dev handoff.
  • Developer resource integration: Automatically attach, update, or remove dev resources linked to Figma nodes, bridging the gap between design and development with contextual documentation or code references.
  • Webhook setup and automation: Enable your agent to create or delete webhooks for team events, making it easy to trigger notifications or workflows based on design activity.
  • Collaborative variable management: Empower the agent to batch-create, modify, or delete variables, collections, and modes across your design system, keeping everything consistent and up to date.

Supported Tools & Triggers

Tools
Add a comment to a filePosts a new comment to a figma file or branch, optionally replying to an existing root comment (replies cannot be nested); `region height` and `region width` in `client meta` must be positive if defining a comment region.
Add a reaction to a commentPosts a specified emoji reaction to an existing comment in a figma file or branch, requiring valid file key and comment id.
Create a webhookCreates a figma webhook for a `team id` to send post notifications for an `event type` to a publicly accessible https `endpoint`; an initial ping is sent unless `status` is `paused`.
Create dev resourcesCreates and attaches multiple uniquely-urled development resources to specified figma nodes, up to 10 per node.
Create, modify, or delete variablesManages variables, collections, modes, and their values in a figma file via batch create/update/delete operations; use temporary ids to link new related items in one request and ensure `variablemodevalues` match the target variable's `resolvedtype`.
Delete a commentDeletes a specific comment from a figma file or branch, provided the authenticated user is the original author of the comment.
Delete a reactionDeletes a specific emoji reaction from a comment in a figma file; the user must have originally created the reaction.
Delete a webhookPermanently deletes an existing webhook, identified by its unique `webhook id`; this operation is irreversible.
Delete dev resourceDeletes a development resource (used to link figma design elements to external developer information like code or tasks) from a specified figma file.
Design tokens to tailwindConvert design tokens to tailwind css configuration.
Detect backgroundDetect background layers for selected nodes.
Discover Figma Resources🔍 smart figma resource discovery - never guess ids again!
Download Figma ImagesDownload images from figma file nodes.
Extract design tokensExtract design tokens from figma files.
Extract Prototype InteractionsExtract prototype interactions and animations from figma files.
Get activity logsRetrieves activity log events from figma, allowing filtering by event types, time range, and pagination.
Get a webhookRetrieves detailed information about a specific webhook by its id, provided the webhook exists and is accessible to the user.
Get comments in a fileRetrieves all comments from an existing figma file, identified by a valid `file key`, returning details like content, author, position, and reactions, with an option for markdown formatted content.
Get componentGet component data with automatic simplification.
Get component setRetrieves detailed metadata for a specific published figma component set using its unique `key`.
Get current userRetrieves detailed information for the currently authenticated figma user.
Get dev resourcesRetrieves development resources (e.
Get file componentsRetrieves published components from a figma file, which must be a main file (not a branch) acting as a library.
Get file component setsRetrieves all published component sets from the specified figma main file (file key must not be for a branch).
Get file jsonGet figma file data with automatic simplification.
Get files in a projectFetches a list of files in a figma project, optionally including branch metadata.
Get file stylesRetrieves a list of published styles (like colors, text attributes, effects, and layout grids) from a specified main figma file (not a branch).
Get image fillsRetrieves temporary (14-day expiry) download urls for all image fills in a figma file; requires `imageref` from `paint` objects to map urls.
Get library analytics component action dataRetrieves component insertion and detachment analytics for a specified figma library, groupable by 'component' or 'team' and filterable by a date range (yyyy-mm-dd).
Get library analytics component usage dataRetrieves component usage analytics for a specified figma library file (identified by `file key`), with data groupable by 'component' or 'file'.
Get library analytics style action dataRetrieves style usage analytics (insertions, detachments) for a figma library, grouped by 'style' or 'team'; if providing a date range, ensure end date is not before start date.
Get library analytics style usage dataRetrieves style usage analytics for a figma library (specified by a valid `file key`), allowing data to be grouped by 'file' or 'style'.
Get library analytics variable action dataRetrieves weekly, paginated analytics data on variable insertions and detachments for a specified figma library (identified by `file key`), groupable by 'variable' or 'team', and filterable by an optional date range.
Get library analytics variable usage dataRetrieves paginated analytics data on variable usage from a specified figma library, grouped by 'file' or 'variable', for libraries with enabled analytics.
Get local variablesRetrieves all local/remote variables for a figma file/branch; crucial for obtaining mode-specific values which `/v1/files/{file key}/variables/published` omits.
Get paymentsRetrieves a user's payment information for a figma plugin, widget, or community file; the authenticated identity must own the resource.
Get projects in a teamRetrieves projects within a specified figma team that are visible to the authenticated user.
Get published variablesRetrieves variables published from a specified figma file; this api is available only to full members of enterprise organizations.
Get reactions for a commentRetrieves reactions for a specific comment in a figma file.
Get styleRetrieves detailed metadata for a specific style in figma using its unique style key.
Get team componentsRetrieves components published in a specific figma team's library; the team must have published components, otherwise an empty list is returned.
Get team component setsRetrieves a paginated list of published component sets (collections of reusable ui elements) from a specified figma team's library.
Get team stylesRetrieves a paginated list of published styles, such as colors or text attributes, from a specified figma team's library.
Get team webhooksRetrieves all webhooks registered for a specified figma team.
Get versions of a fileRetrieves the version history for a figma file or branch, as specified by its `file key`.
Get webhook requestsRetrieves a history of webhook requests for a specific figma webhook subscription; data is available for requests sent within the last seven days.
Render images of file nodesRenders specified nodes from a figma file as images (jpg, pdf, png, svg), returning a map of node ids to image urls (or `null` for failed nodes); images expire after 30 days and are capped at 32 megapixels (larger requests are scaled down).
Update a webhookUpdates an existing figma webhook, identified by `webhook id`, allowing modification of its event type, endpoint, passcode, status, or description.
Update dev resourcesUpdates the name and/or url of one or more existing figma dev resources, each identified by its unique `id`.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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 Figma 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 Figma 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 Figma 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 Figma session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["figma"]
    )
    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 Figma 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 Figma assistant agent with MCP tools
    agent = AssistantAgent(
        name="figma_assistant",
        description="An AI assistant that helps with Figma 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 Figma 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 Figma 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 Figma 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 Figma 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 Figma session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["figma"]
    )
    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 Figma assistant agent with MCP tools
        agent = AssistantAgent(
            name="figma_assistant",
            description="An AI assistant that helps with Figma 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 Figma 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 Figma 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 Figma, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Figma MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Figma MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Figma MCP with Claude Agent SDK

Google ADK

Google ADK

Use Figma MCP with Google ADK

LangChain

LangChain

Use Figma MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Figma MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Figma MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Figma MCP with LlamaIndex

CrewAI

CrewAI

Use Figma MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Figma MCP?

With a standalone Figma MCP server, the agents and LLMs can only access a fixed set of Figma tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Figma 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 Figma tools.

Can I manage the permissions and scopes for Figma while using Tool Router?

Yes, absolutely. You can configure which Figma 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 Figma data and credentials are handled as safely as possible.

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HubSpot
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Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
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HubSpot
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Altera
DataStax
Entelligence
Rolai

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