# How to integrate Figma MCP with LlamaIndex

```json
{
  "title": "How to integrate Figma MCP with LlamaIndex",
  "toolkit": "Figma",
  "toolkit_slug": "figma",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/figma/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/figma/framework/llama-index.md",
  "updated_at": "2026-05-12T10:11:20.573Z"
}
```

## 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.

## Also integrate Figma with

- [ChatGPT](https://composio.dev/toolkits/figma/framework/chatgpt)
- [Antigravity](https://composio.dev/toolkits/figma/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/figma/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/figma/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/figma/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/figma/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/figma/framework/codex)
- [Cursor](https://composio.dev/toolkits/figma/framework/cursor)
- [VS Code](https://composio.dev/toolkits/figma/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/figma/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/figma/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/figma/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/figma/framework/cli)
- [Google ADK](https://composio.dev/toolkits/figma/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/figma/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/figma/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/figma/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/figma/framework/crew-ai)

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FIGMA_ADD_A_COMMENT_TO_A_FILE` | 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. |
| `FIGMA_ADD_A_REACTION_TO_A_COMMENT` | 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. |
| `FIGMA_CREATE_A_WEBHOOK` | Create a webhook | Creates a Figma webhook to receive POST notifications when specific events occur. Webhooks can monitor events at three scopes: - Team level: monitors all files in a team (requires team admin permissions) - Project level: monitors all files in a project (requires edit access) - File level: monitors a specific file (requires edit access) Upon creation, Figma sends an initial PING event to verify your endpoint (unless status is PAUSED). IMPORTANT: team_id, project_id, and file_key cannot be discovered programmatically. Extract them from Figma URLs or use FIGMA_DISCOVER_FIGMA_RESOURCES to parse URLs. |
| `FIGMA_CREATE_DEV_RESOURCES` | Create dev resources | Creates and attaches multiple uniquely-URLed development resources to specified Figma nodes, up to 10 per node. |
| `FIGMA_CREATE_MODIFY_DELETE_VARIABLES` | 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`. |
| `FIGMA_DELETE_A_COMMENT` | Delete a comment | Deletes a specific comment from a Figma file or branch, provided the authenticated user is the original author of the comment. |
| `FIGMA_DELETE_A_REACTION` | Delete a reaction | Deletes a specific emoji reaction from a comment in a Figma file; the user must have originally created the reaction. |
| `FIGMA_DELETE_A_WEBHOOK` | Delete a webhook | Permanently deletes an existing webhook, identified by its unique `webhook_id`; this operation is irreversible. |
| `FIGMA_DELETE_DEV_RESOURCE` | 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. |
| `FIGMA_DESIGN_TOKENS_TO_TAILWIND` | Design tokens to tailwind | Convert design tokens to Tailwind CSS configuration. TWO-STEP WORKFLOW: 1. First, call FIGMA_EXTRACT_DESIGN_TOKENS with a Figma file_key to extract design tokens 2. Then, pass the returned DesignTokens object to this action's 'tokens' parameter This action generates: - tailwind.config.ts/js with theme extensions - Optional globals.css with font imports Note: Shadow colors can be provided in either string format (e.g., "rgba(15, 110, 110, 0.32)") or dictionary format (e.g., {"r": 0.059, "g": 0.431, "b": 0.431, "a": 0.32}). |
| `FIGMA_DETECT_BACKGROUND` | Detect Background Layers | Detect background layers for selected nodes in a Figma file. This action analyzes the Figma document structure and identifies potential background elements for the given target nodes. It uses: - Geometric analysis (bounding box overlap/containment) - Z-index ordering (nodes earlier in the layer stack are behind later ones) - Visual properties (fills, node types) - Naming conventions (nodes named 'background', 'bg', etc.) Returns background candidates with confidence scores (0-1) and explanations. |
| `FIGMA_DISCOVER_FIGMA_RESOURCES` | Discover Figma Resources | Smart Figma resource discovery - extract IDs from any Figma URL. Supports all URL formats: /file/, /design/, /board/, /proto/, /slides/ Example: figma.com/board/ABC123/Name → file_key=ABC123 Discovery workflow: team_id → projects → files → nodes Use extracted IDs with GetFileJson, DetectBackground, etc. |
| `FIGMA_DOWNLOAD_FIGMA_IMAGES` | Download Figma Images | Download images from Figma file nodes. Renders specified nodes as images and downloads them. Supports PNG, SVG, JPG, and PDF formats. REQUIRED PARAMETERS: - file_key (string): The Figma file key from the URL - images (array): List of objects, each containing: - node_id (string, required): The node ID to export (e.g., "1:2") - file_name (string, required): Output filename with extension (e.g., "logo.png") - format (string, optional): One of 'png', 'svg', 'jpg', 'pdf'. Defaults to 'png' Example usage: { "file_key": "abc123XYZ", "images": [ {"node_id": "1:2", "file_name": "logo.png", "format": "png"} ] } To find node IDs, use FIGMA_GET_FILE_JSON or look in Figma URLs after 'node-id='. NOTE: Returned image URLs expire shortly after generation — download them immediately. |
| `FIGMA_EXTRACT_DESIGN_TOKENS` | Extract design tokens | Extract design tokens from Figma files by combining styles, variables, and node-extracted values. Only values defined as Figma styles or variables are captured — any design values not encoded as styles/variables are silently omitted. Requires `file_variables:read` scope and a Figma plan that supports variables for full output; if variables return empty, supplement with FIGMA_GET_LOCAL_VARIABLES. |
| `FIGMA_EXTRACT_PROTOTYPE_INTERACTIONS` | Extract Prototype Interactions | Extract prototype interactions and animations from Figma files. Required parameter: - file_key: The Figma file key extracted from a URL like 'https://www.figma.com/file/ABC123xyz/MyFile' (the 'ABC123xyz' part) Analyzes the prototype data to extract: - User interactions (clicks, hovers, etc.) - Transition animations - Component variant states - User flows and navigation |
| `FIGMA_GET_ACTIVITY_LOGS` | Get activity logs | Retrieves activity log events from Figma, allowing filtering by event types, time range, and pagination. |
| `FIGMA_GET_A_WEBHOOK` | Get a webhook | Retrieves detailed information about a specific webhook by its ID, provided the webhook exists and is accessible to the user. |
| `FIGMA_GET_COMMENTS_IN_A_FILE` | 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. |
| `FIGMA_GET_COMPONENT2` | Get component | Fetches metadata for a specific component using its unique identifier. Use when you need to retrieve published component details from a team library. |
| `FIGMA_GET_COMPONENT_SET` | Get component set | Retrieves detailed metadata for a specific published Figma component set using its unique `key`. |
| `FIGMA_GET_CURRENT_USER` | Get current user | Retrieves detailed information for the currently authenticated Figma user. |
| `FIGMA_GET_DEV_RESOURCES` | Get dev resources | Retrieves development resources (e.g., Jira/GitHub links) for a Figma main file, optionally filtering by specific node IDs. |
| `FIGMA_GET_FILE_COMPONENTS` | Get file components | Retrieves published components from a Figma file, which must be a main file (not a branch) acting as a library. |
| `FIGMA_GET_FILE_COMPONENT_SETS` | Get file component sets | Retrieves all published component sets from the specified Figma main file (file_key must not be for a branch). |
| `FIGMA_GET_FILE_JSON` | Get file json | Get Figma Design file data with automatic simplification. IMPORTANT: Only supports Design files (figma.com/design/{file_key}). FigJam board files (figma.com/board/{file_key}) and Slides files (figma.com/slides/{file_key}) are NOT supported and will return a 400 error: "File type not supported by this endpoint". This enhanced version automatically transforms verbose Figma JSON into clean, AI-friendly format with: - CSS-like property names - Deduplicated variables - Removed empty values - 70%+ size reduction Use simplify=False to get raw API response. |
| `FIGMA_GET_FILE_METADATA` | Get file metadata | Get Figma file metadata including name, creator, last modification details, thumbnail, and access information. Use when you need quick file overview without the full document tree. |
| `FIGMA_GET_FILE_NODES` | Get file nodes | Fetch JSON for specific node IDs from a Figma file to avoid full-file payload limits. Use when you already know target node IDs (from shallow file fetch or component listings) or when full-file JSON has hit payload limits. Prefer depth=1 for fast discovery. |
| `FIGMA_GET_FILES_IN_A_PROJECT` | Get files in a project | Fetches a list of files in a Figma project, optionally including branch metadata. |
| `FIGMA_GET_FILE_STYLES` | 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). |
| `FIGMA_GET_IMAGE_FILLS` | 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. |
| `FIGMA_GET_LIBRARY_ANALYTICS_COMPONENT_ACTION_DATA` | 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). |
| `FIGMA_GET_LIBRARY_ANALYTICS_COMPONENT_USAGE_DATA` | 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'. |
| `FIGMA_GET_LIBRARY_ANALYTICS_STYLE_ACTION_DATA` | 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. |
| `FIGMA_GET_LIBRARY_ANALYTICS_STYLE_USAGE_DATA` | Get library analytics style usage data | Retrieves style usage analytics for a published Figma library. Returns data about how styles (colors, text styles, effects, grids) from the library are being used across your organization. Requires Enterprise plan and library_analytics:read scope. Group results by 'style' to see per-style metrics or by 'file' to see which files use the library's styles. |
| `FIGMA_GET_LIBRARY_ANALYTICS_VARIABLE_ACTION_DATA` | 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. Note: Requires Enterprise plan and library_analytics:read scope. |
| `FIGMA_GET_LIBRARY_ANALYTICS_VARIABLE_USAGE_DATA` | 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. |
| `FIGMA_GET_LOCAL_VARIABLES` | 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. |
| `FIGMA_GET_PAYMENTS` | Get payments | Retrieves a user's payment information for a Figma plugin, widget, or Community file; the authenticated identity must own the resource. |
| `FIGMA_GET_PROJECTS_IN_A_TEAM` | Get projects in a team | Retrieves projects within a specified Figma team that are visible to the authenticated user. |
| `FIGMA_GET_PUBLISHED_VARIABLES` | Get published variables | Retrieves variables published from a specified Figma file; this API is available only to full members of Enterprise organizations. |
| `FIGMA_GET_REACTIONS_FOR_A_COMMENT` | Get reactions for a comment | Retrieves reactions for a specific comment in a Figma file. |
| `FIGMA_GET_SCIM_SERVICE_PROVIDER_CONFIG` | Get SCIM service provider config | Get Figma's SCIM service provider configuration. Returns configuration details including which SCIM operations are supported (patch, bulk, filter, etc.), authentication schemes, and service capabilities. |
| `FIGMA_GET_STYLE` | Get style | Retrieves detailed metadata for a specific style in Figma using its unique style key. |
| `FIGMA_GET_TEAM_COMPONENTS` | 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. |
| `FIGMA_GET_TEAM_COMPONENT_SETS` | Get team component sets | Retrieves a paginated list of published component sets (collections of reusable UI elements) from a specified Figma team's library. |
| `FIGMA_GET_TEAM_STYLES` | Get team styles | Retrieves a paginated list of published styles (fill colors, text styles, effects, grids) from a specified Figma team's library. Note: The team must have published styles in its library for this endpoint to return data. Teams without published styles will return an empty list. |
| `FIGMA_GET_TEAM_WEBHOOKS` | Get webhooks | Retrieves all webhooks registered for a specified Figma context (team, project, or file). Uses the Figma Webhooks V2 API endpoint (GET /v2/webhooks) with context and context_id query parameters. This is the recommended approach as the legacy path-based endpoint (/v2/teams/{team_id}/webhooks) is deprecated. Note: team_id, project_id, and file_key cannot be discovered programmatically via the API. Extract them from Figma URLs or use FIGMA_DISCOVER_FIGMA_RESOURCES to parse URLs. |
| `FIGMA_GET_VERSIONS_OF_A_FILE` | Get versions of a file | Retrieves the version history for a Figma file or branch, as specified by its `file_key`. |
| `FIGMA_GET_WEBHOOK_REQUESTS` | 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. |
| `FIGMA_RENDER_IMAGES_OF_FILE_NODES` | Render images of file nodes | Render Figma nodes as images (PNG, JPG, SVG, PDF). Returns a map of node IDs to temporary image URLs (valid for 30 days). Images are capped at 32 megapixels; larger requests are automatically scaled down. REQUIRED PARAMETERS: - file_key: Figma file key from URL (e.g., 'abc123XYZ' from figma.com/design/abc123XYZ/...) - ids: Comma-separated node IDs to render (e.g., '1:2' or '1:2,1:3,1:4') NODE IDs: Found in Figma URLs after 'node-id=' or from FIGMA_GET_FILE_JSON response. FORMATS: - png/jpg: Raster images with optional scale (0.01-4.0) - svg: Vector graphics with text outline options - pdf: Document format COMMON ISSUES: - null value in images map = node failed to render (invalid ID, invisible, 0% opacity) - 404 error = file_key not found or no access - 429 error = rate limit exceeded, wait and retry |
| `FIGMA_UPDATE_A_WEBHOOK` | Update a webhook | Updates an existing Figma webhook, identified by `webhook_id`, allowing modification of its event type, endpoint, passcode, status, or description. |
| `FIGMA_UPDATE_DEV_RESOURCES` | Update dev resources | Updates the name and/or URL of one or more existing Figma Dev Resources, each identified by its unique `id`. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Figma MCP server is an implementation of the Model Context Protocol that connects your AI agent to Figma. It provides structured and secure access so your agent can perform Figma operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 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

### 1. Getting API Keys for OpenAI, Composio, and Figma

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

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

### 3. Set environment variables

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

### 4. Import modules

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
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")
```

```typescript
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");
```

### 6. Create a Tool Router session and build the agent function

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.
```python
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=["figma"],
    )

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

```typescript
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;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
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}")
```

```typescript
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();
}
```

### 8. Define the main entry point

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
```python
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!")
```

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

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Figma, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
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=["figma"],
    )

    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 Figma actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Figma 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!")
```

```typescript
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.

## How to build Figma MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/figma/framework/chatgpt)
- [Antigravity](https://composio.dev/toolkits/figma/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/figma/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/figma/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/figma/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/figma/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/figma/framework/codex)
- [Cursor](https://composio.dev/toolkits/figma/framework/cursor)
- [VS Code](https://composio.dev/toolkits/figma/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/figma/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/figma/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/figma/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/figma/framework/cli)
- [Google ADK](https://composio.dev/toolkits/figma/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/figma/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/figma/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/figma/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/figma/framework/crew-ai)

## Related Toolkits

- [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams.
- [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content.
- [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates.
- [Canva](https://composio.dev/toolkits/canva) - Canva is a drag-and-drop design suite for creating professional graphics, presentations, and marketing materials. It makes it easy for anyone to design with beautiful templates and a vast library of elements.
- [Claid ai](https://composio.dev/toolkits/claid_ai) - Claid.ai delivers AI-driven image editing APIs for tasks like background removal, upscaling, and color correction. It helps automate and enhance image workflows with powerful, developer-friendly tools.
- [Cloudinary](https://composio.dev/toolkits/cloudinary) - Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally.
- [Cults](https://composio.dev/toolkits/cults) - Cults is a digital marketplace for 3D printing models, connecting designers and makers. It lets creators share, sell, and discover a huge variety of printable designs easily.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.
- [Dreamstudio](https://composio.dev/toolkits/dreamstudio) - DreamStudio is Stability AI’s platform for generating and editing images with AI. It lets you easily turn ideas into stunning visuals, fast.
- [Dynapictures](https://composio.dev/toolkits/dynapictures) - Dynapictures is a cloud-based platform for generating personalized images at scale. Instantly create hundreds of custom visuals using your data sources, like Google Sheets.
- [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools.
- [Gamma](https://composio.dev/toolkits/gamma) - Gamma is an AI-powered platform for making beautiful, interactive presentations and documents. It lets anyone create and share engaging decks with minimal effort.
- [Html to image](https://composio.dev/toolkits/html_to_image) - Html to image converts HTML and CSS into images or captures web page screenshots. Instantly generate visuals from code or web content—no manual screenshots needed.
- [Imagior](https://composio.dev/toolkits/imagior) - Imagior is an AI-powered image generation platform that lets you create and customize images using dynamic templates and APIs. Perfect for businesses and creators needing fast, scalable visuals without design hassle.
- [Imejis io](https://composio.dev/toolkits/imejis_io) - Imejis io is an API-based image generation platform with powerful customization and template support. It lets you create and modify images in seconds, no manual design work required.
- [Imgix](https://composio.dev/toolkits/imgix) - Imgix is a real-time image processing and delivery service for developers. It helps you optimize, transform, and deliver images efficiently at any scale.
- [Kraken io](https://composio.dev/toolkits/kraken_io) - Kraken.io is an image optimization and compression platform. It helps you shrink image file sizes while keeping visual quality intact.
- [Logo dev](https://composio.dev/toolkits/logo_dev) - Logo.dev is an API and database for high-resolution company logos and brand metadata. Instantly fetch official logos from any domain without scraping or manual searching.
- [Miro](https://composio.dev/toolkits/miro) - Miro is a collaborative online whiteboard platform for teams to brainstorm, design, and manage projects visually. It streamlines teamwork by enabling real-time idea sharing, diagramming, and workflow planning in a single space.
- [Mural](https://composio.dev/toolkits/mural) - Mural is a digital whiteboard platform for distributed visual collaboration. It helps teams brainstorm, map ideas, and diagram together in real time.

## Frequently Asked Questions

### 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 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 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.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)