Figma MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Figma as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Figma. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Figma?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Figma MCP Server
Figma is the leading collaborative interface design tool. This MCP server allows your AI agent to interact with your Figma files, projects, and teams flawlessly.
LlamaIndex agents combine Figma tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
Key Features
- File & Node Inspection — Retrieve the full document tree or specific layers to analyze design structures flawlessly.
- Image Rendering — Render Figma frames, components, or layers into PNG, SVG, or PDF images flawlessly native.
- Team & Project Orchestration — List team projects and project files to navigate your design workspace flawlessly.
- Design Token Access — Extract published components, styles, and local variables to sync with codebases flawlessly.
- Collaboration Tools — Read and post comments directly on design files to keep feedback loops active synchronously.
- Version History — Access file version history to track design evolutions flawlessy through the agent.
The Figma MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Figma to LlamaIndex via MCP
Follow these steps to integrate the Figma MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from Figma
Why Use LlamaIndex with the Figma MCP Server
LlamaIndex provides unique advantages when paired with Figma through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Figma tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Figma tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Figma, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Figma tools were called, what data was returned, and how it influenced the final answer
Figma + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Figma MCP Server delivers measurable value.
Hybrid search: combine Figma real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Figma to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Figma for fresh data
Analytical workflows: chain Figma queries with LlamaIndex's data connectors to build multi-source analytical reports
Figma MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Figma to LlamaIndex via MCP:
get_comments
Get comments on a Figma file
get_file
Use depth to limit node traversal (1=pages only, 2=pages+top frames). Get a Figma file
get_file_nodes
Get specific nodes from a Figma file
get_file_versions
List versions of a Figma file
get_images
Render nodes from a Figma file as images
get_local_variables
List design tokens/variables in a Figma file
get_me
Get details for the authorized Figma user
list_components
List published team components
list_project_files
List files in a project
list_styles
List published team styles
list_team_projects
List projects in a Figma team
post_comment
Post a comment on a Figma file
Example Prompts for Figma in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Figma immediately.
"List all projects in my Figma team ID 123456."
"Get the document tree for file key abcDEF123."
"Render nodes 1:2 and 1:5 as PNG images."
Troubleshooting Figma MCP Server with LlamaIndex
Common issues when connecting Figma to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFigma + LlamaIndex FAQ
Common questions about integrating Figma MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Figma with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Figma to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.