How to integrate Figma MCP with CrewAI

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Introduction

This guide walks you through connecting Figma to CrewAI 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 CrewAI 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 a Composio API key and configure your Figma connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Figma
  • Build a conversational loop where your agent can execute Figma operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Figma connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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 crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Figma via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Figma MCP URL

Create a Composio Tool Router session for Figma

python
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:
  • You create a Figma only session through Composio
  • Composio returns an MCP HTTP URL that exposes Figma tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Figma Assistant",
    goal="Help users interact with Figma through natural language commands",
    backstory=(
        "You are an expert assistant with access to Figma tools. "
        "You can perform various Figma operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Figma MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Figma operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Figma related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_figma_agent.py

Complete Code

Here's the complete code to get you started with Figma and CrewAI:

python
# file: crewai_figma_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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 LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Figma assistant agent
    toolkit_agent = Agent(
        role="Figma Assistant",
        goal="Help users interact with Figma through natural language commands",
        backstory=(
            "You are an expert assistant with access to Figma tools. "
            "You can perform various Figma operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Figma operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Figma related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Figma through Composio's Tool Router. The agent can perform Figma operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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

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 CrewAI?

Yes, you can. CrewAI 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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