How to integrate Figma MCP with LlamaIndex

This guide walks you through connecting Figma to LlamaIndex 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 through natural language commands. This guide will help you understand how to give your LlamaIndex 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.

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Introduction

This guide walks you through connecting Figma to LlamaIndex 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 through natural language commands.

This guide will help you understand how to give your LlamaIndex 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.

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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 Figma
  • Connect LlamaIndex to the Figma MCP server
  • Build a Figma-powered agent using LlamaIndex
  • Interact with Figma 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 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.

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

Step by step10 STEPS
1

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 Figma account and project
  • Basic familiarity with async Python/Typescript
2

Getting API Keys for OpenAI, Composio, and Figma

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
3

Installing dependencies

npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv

Create a new Typescript project and install the necessary dependencies:

  • @composio/llamaindex: Composio's LlamaIndex integration
  • @llamaindex/openai: OpenAI LLM integration
  • @llamaindex/tools: MCP client for LlamaIndex
  • @llamaindex/workflow: Workflow framework for LlamaIndex
  • dotenv: Environment variable management
4

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 Figma access
5

Import modules

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

Create a new file called figma_llamaindex_agent.ts and import the required modules:

Key imports:

  • dotenv.config loads .env at runtime
  • readline gives us a simple CLI chat loop
  • Composio is the main Composio SDK client
  • mcp connects to an MCP endpoint
  • createAgent builds a LlamaIndex agent
  • openai configures the LLM backend
6

Load environment variables and initialize Composio

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");

What's happening:

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

7

Create a Tool Router session and build the agent function

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["figma"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Figma actions." ,
    llm,
    tools,
  });

  return agent;
}

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, figma)
  • 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 Figma tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
8

Create an interactive chat loop

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

What's happening:

  • We're creating a direct terminal interface to chat with Figma
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • The agent processes the request, selects appropriate Figma tools, and returns a result
  • We extract the answer from the result data structure and display it to the user
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are streamed in a clear, readable format
9

Define the main entry point

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();

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 Figma
10

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

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

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["figma"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Figma actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();

Conclusion

You've successfully connected Figma to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Figma 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.
TOOLS

Supported Tools

Every Figma action and event your agent gets out of the box.

Add a comment to a file

Posts 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 comment

Posts a specified emoji reaction to an existing comment in a Figma file or branch, requiring valid file_key and comment_id.

Create a webhook

Creates a Figma webhook to receive POST notifications when specific events occur.

Create dev resources

Creates and attaches multiple uniquely-URLed development resources to specified Figma nodes, up to 10 per node.

Create, modify, or delete variables

Manages 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 comment

Deletes a specific comment from a Figma file or branch, provided the authenticated user is the original author of the comment.

Delete a reaction

Deletes a specific emoji reaction from a comment in a Figma file; the user must have originally created the reaction.

Delete a webhook

Permanently deletes an existing webhook, identified by its unique `webhook_id`; this operation is irreversible.

Delete dev resource

Deletes 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 tailwind

Convert design tokens to Tailwind CSS configuration.

Detect Background Layers

Detect background layers for selected nodes in a Figma file.

Discover Figma Resources

Smart Figma resource discovery - extract IDs from any Figma URL.

Download Figma Images

Download images from Figma file nodes.

Extract design tokens

Extract design tokens from Figma files by combining styles, variables, and node-extracted values.

Extract Prototype Interactions

Extract prototype interactions and animations from Figma files.

Get activity logs

Retrieves activity log events from Figma, allowing filtering by event types, time range, and pagination.

Get a webhook

Retrieves detailed information about a specific webhook by its ID, provided the webhook exists and is accessible to the user.

Get comments in a file

Retrieves 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 component

Fetches metadata for a specific component using its unique identifier.

Get component set

Retrieves detailed metadata for a specific published Figma component set using its unique `key`.

Get current user

Retrieves detailed information for the currently authenticated Figma user.

Get dev resources

Retrieves development resources (e.

Get file components

Retrieves published components from a Figma file, which must be a main file (not a branch) acting as a library.

Get file component sets

Retrieves all published component sets from the specified Figma main file (file_key must not be for a branch).

Get file json

Get Figma Design file data with automatic simplification.

Get file metadata

Get Figma file metadata including name, creator, last modification details, thumbnail, and access information.

Get file nodes

Fetch JSON for specific node IDs from a Figma file to avoid full-file payload limits.

Get files in a project

Fetches a list of files in a Figma project, optionally including branch metadata.

Get file styles

Retrieves a list of published styles (like colors, text attributes, effects, and layout grids) from a specified main Figma file (not a branch).

Get image fills

Retrieves 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 data

Retrieves 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 data

Retrieves 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 data

Retrieves 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 data

Retrieves style usage analytics for a published Figma library.

Get library analytics variable action data

Retrieves 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 data

Retrieves paginated analytics data on variable usage from a specified Figma library, grouped by 'file' or 'variable', for libraries with enabled analytics.

Get local variables

Retrieves all local/remote variables for a Figma file/branch; crucial for obtaining mode-specific values which `/v1/files/{file_key}/variables/published` omits.

Get payments

Retrieves a user's payment information for a Figma plugin, widget, or Community file; the authenticated identity must own the resource.

Get projects in a team

Retrieves projects within a specified Figma team that are visible to the authenticated user.

Get published variables

Retrieves variables published from a specified Figma file; this API is available only to full members of Enterprise organizations.

Get reactions for a comment

Retrieves reactions for a specific comment in a Figma file.

Get SCIM service provider config

Get Figma's SCIM service provider configuration.

Get style

Retrieves detailed metadata for a specific style in Figma using its unique style key.

Get team components

Retrieves components published in a specific Figma team's library; the team must have published components, otherwise an empty list is returned.

Get team component sets

Retrieves a paginated list of published component sets (collections of reusable UI elements) from a specified Figma team's library.

Get team styles

Retrieves a paginated list of published styles (fill colors, text styles, effects, grids) from a specified Figma team's library.

Get webhooks

Retrieves all webhooks registered for a specified Figma context (team, project, or file).

Get versions of a file

Retrieves the version history for a Figma file or branch, as specified by its `file_key`.

Get webhook requests

Retrieves 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 nodes

Render Figma nodes as images (PNG, JPG, SVG, PDF).

Update a webhook

Updates an existing Figma webhook, identified by `webhook_id`, allowing modification of its event type, endpoint, passcode, status, or description.

Update dev resources

Updates the name and/or URL of one or more existing Figma Dev Resources, each identified by its unique `id`.

FRAMEWORKS

How to build Figma MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Figma MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Figma MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Figma MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Figma MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Figma MCP with Claude Cowork

Codex logo

Codex

Use Figma MCP with Codex

Cursor logo

Cursor

Use Figma MCP with Cursor

OpenClaw logo

OpenClaw

Use Figma MCP with OpenClaw

Hermes logo

Hermes

Use Figma MCP with Hermes

CLI logo

CLI

Use Figma MCP with CLI

Google ADK logo

Google ADK

Use Figma MCP with Google ADK

LangChain logo

LangChain

Use Figma MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Figma MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Figma MCP with Mastra AI

CrewAI logo

CrewAI

Use Figma MCP with CrewAI

OpenCode logo

OpenCode

Use Figma MCP with OpenCode

VS Code logo

VS Code

Use Figma MCP with VS Code

FAQ

Frequently asked questions

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.

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 Figma tools.

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.

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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