Figma To Code MCP

Why This Project?

Figma To Code MCP specializes in extracting only the information LLMs need to build UIs while removing Figma-specific metadata that isn't relevant for code generation. The result:

• ✅ 99.5% size reduction on real Figma files (65 MB → 128 KB)

• ✅ CSS-aligned property names (backgroundColor, flexDirection, etc.) matching LLM training data

• ✅ Complete UI-building data preserved (layout, styling, text, components)

• ✅ Inline styles - no separate dictionaries to parse

• ✅ Omits Figma internals - no bounding boxes, constraints, or prototype data

• ✅ Variable resolution - resolves Figma variables to actual values

• ✅ SVG support - exports vector graphics to disk

• ✅ Pattern collapsing - deduplicates repeating UI patterns

Give Cursor and other AI-powered coding tools access to your Figma files with this Model Context Protocol server.

Related MCP server: Framelink Figma MCP Server

Available Tools

Required Scopes

Create a Figma personal access token with these scopes:

Note: Variable resolution requires Enterprise plan. Set resolveVariables: false if not on Enterprise.

How it works

1. Open your IDE's chat (e.g. agent mode in Cursor).

2. Paste a link to a Figma file, frame, or group.

3. Ask Cursor to implement the design.

4. Cursor fetches CSS-aligned, LLM-optimized design data and generates accurate code.

This MCP server transforms Figma API data into an LLM-friendly format:

• CSS property names (backgroundColor, flexDirection, fontSize) instead of Figma internals

• Inline styles directly in nodes (no separate dictionaries)

• Flexbox primitives for layout (no absolute positioning)

• Complete UI data (colors, typography, spacing, effects)

• 99.5% size reduction while preserving all UI-critical information

See V2_CSS_PROPERTY_MAPPING.md for complete property mapping details.

Getting Started

Many code editors and other AI clients use a configuration file to manage MCP servers.

The tmegit-figma-to-code-mcp server can be configured by adding the following to your configuration file.

MacOS / Linux

{
  "mcpServers": {
    "Figma To Code MCP": {
      "command": "npx",
      "args": ["-y", "@tmegit/figma-to-code-mcp", "--figma-api-key=YOUR-KEY", "--stdio"]
    }
  }
}

Windows

{
  "mcpServers": {
    "Figma To Code MCP": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "@tmegit/figma-to-code-mcp",
        "--figma-api-key=YOUR-KEY",
        "--stdio"
      ]
    }
  }
}

Or you can set FIGMA_API_KEY and PORT in the env field.

Configuration

The server reads configuration from CLI flags and environment variables. If both are set, the CLI flag wins.

Notes:

• --library-file-keys and FIGMA_LIBRARY_VARIABLE_PREFETCH_FILE_KEYS are comma-separated Figma library file keys.

• FIGMA_MCP_CACHE_PATH may point to either a file or a directory. If it is a directory, the cache file is stored as figma-mcp-library-cache.json inside it.

• The library cache is used only when library file keys are configured.

• FIGMA_MCP_REFRESH_CACHE forces a re-fetch on startup even if a cache file exists.

Example .env:

FIGMA_API_KEY=your_figma_pat
# prefetch variables (tokens etc) from specific library files on startup to avoid T2 calls during design fetch
FIGMA_LIBRARY_VARIABLE_PREFETCH_FILE_KEYS=abc123,def456
FIGMA_MCP_CACHE_PATH=./cache
FIGMA_MCP_CACHE_TTL_MS=604800000
# Uncomment to force cache refresh on next startup
# FIGMA_MCP_REFRESH_CACHE=1

API Calls & Rate Limits

One execution of get_figma_design makes the following API calls:

Amount of T1 calls: 1 + N (N=number of unique library files) Amount of T2 calls: 1 (if resolveVariables=true) Amount of T3 calls: 2 + N (styles + component key resolution + N library components)

For Professional plan with Dev/Full seat: 10 req/min (Tier 1), 25 req/min (Tier 2), 50 req/min (Tier 3).

Star History

Acknowledgment

This project was initially inspired by the ideas explored in the original Figma Context MCP by GLips: https://github.com/glips/figma-context-mcp

While the original project provides a Model Context Protocol (MCP) server that simplifies Figma data for use with AI coding agents, this implementation has been substantially redesigned with a different data model, API, and processing approach, and should be considered an independent system.