How to integrate Figma MCP with Pydantic AI

BOOK A CALLGET STARTED FOR FREE
Framework Integration Gradient
Figma Logo
Pydantic AI Logo
divider

Introduction

This guide walks you through connecting Figma to Pydantic AI 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 Pydantic AI 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Figma
  • 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 Figma 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 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 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 Figma
  • 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 Figma
  • MCPServerStreamableHTTP connects to the Figma 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 Figma
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["figma"],
    )
    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 Figma 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
figma_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[figma_mcp],
    instructions=(
        "You are a Figma assistant. Use Figma tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Figma endpoint
  • The agent uses GPT-5 to interpret user commands and perform Figma 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 Figma.\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
  • Figma 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 Figma 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 Figma
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["figma"],
    )
    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
    figma_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[figma_mcp],
        instructions=(
            "You are a Figma assistant. Use Figma 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 Figma.\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 Figma through Composio's Tool Router. With this setup, your agent can perform real Figma 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 + Figma for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

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 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 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.

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free