Community Figma MCP server

Product Introduction

1. Definition: The Community Figma MCP Server is an open-source implementation of the Figma Model Context Protocol (MCP), acting as a bi-directional communication bridge between AI agents (like ChatGPT, Claude) and Figma design documents. It operates as a local middleware server, enabling programmatic creation, editing, and organization of Figma designs.
2. Core Value Proposition: It solves the critical limitation of Figma's official MCP server (read-only access) by providing full read-write capabilities, empowering designers to leverage AI copilots for iterative design tasks directly within Figma documents, significantly accelerating UI/UX workflow automation.

Main Features

1. Real-time Figma Document Manipulation: Enables AI agents to execute commands like creating components, arranging layers, and setting properties via the Figma Plugin API. The server uses a WebSocket-based polling architecture to bypass the plugin sandbox, translating MCP tool calls into Figma plugin actions and returning updated context.
2. Seamless AI Agent Integration: Supports any MCP-compatible client (ChatGPT, Claude Desktop, Cursor, Windsurf) using Streaming HTTP transport. Configuration requires simply pointing the AI agent to the local server endpoint (http://localhost:38450/mcp).
3. Local Execution & Open-Source Transparency: Runs entirely locally on the user's machine (npm run start), ensuring design data privacy. The MIT-licensed GitHub repository provides full visibility into the communication protocol, queue management system, and plugin interaction logic.

Problems Solved

1. Pain Point: Designers lack AI-assisted editing tools within Figma, unlike developers benefiting from AI coding copilots. The official Figma MCP only offers read-only access, hindering automation of repetitive design tasks (component creation, layout adjustments).
2. Target Audience: UI/UX Designers seeking AI workflow augmentation; Design Ops Engineers automating design system updates; Product Teams integrating AI into design-to-development pipelines.
3. Use Cases: Rapidly generating design wireframes from text prompts; auto-organizing complex Figma files; creating reusable component libraries via AI instructions; batch-updating design tokens across multiple frames.

Unique Advantages

1. Differentiation: Unlike the official Figma MCP Server (read-only, cloud-based), this community version offers full read-write functionality executed locally, granting unprecedented control for AI-driven design iteration. It’s the only open-source MCP server enabling Figma edits.
2. Key Innovation: The dual-component architecture – combining a Node.js MCP server (handles tool call queuing/WebSocket comms) with a Figma Plugin (executes sandboxed API actions) – overcomes Figma’s plugin security restrictions. The WebSocket message broker ensures reliable tool call/response synchronization.

Frequently Asked Questions (FAQ)

1. How does the Community Figma MCP Server handle Figma plugin sandbox limitations?
   It employs a WebSocket-mediated polling system: The MCP server queues tool calls, relays them via WebSockets to the Figma plugin, which executes actions and sends results back, enabling indirect read-write access within sandbox constraints.
2. What AI agents are compatible with this Figma MCP Server?
   Any client supporting the Model Context Protocol (MCP) over Streaming HTTP transport works, including ChatGPT (with MCP-enabled tools), Claude Desktop, Cursor.sh, and Windsurf.ai.
3. Is the Community Figma MCP Server secure for commercial projects?
   Yes, it runs locally on your machine (localhost:38450), ensuring design data never leaves your environment. The open-source code allows security audits.
4. Can I edit existing Figma components using this MCP server?
   Absolutely. The server supports editing any Figma node accessible via the Plugin API, including modifying component properties, layer hierarchies, and auto-layout constraints through AI tool calls.
5. What’s required to set up the Figma MCP Server for AI design assistance?
   You need Node.js, a Figma account, and an MCP-compatible AI client. Clone the GitHub repo, build/install the Figma plugin, start the local server, and configure your AI agent’s MCP endpoint.