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

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

Available Tools

Tool	Description
get_figma_design	Fetches CSS-aligned, LLM-optimized design data. Supports SVG export to custom dir.
get_image_fills	Retrieves image fill URLs from a Figma file
render_node_images	Renders Figma nodes as PNG images
read_vector_svg	Reads vector node data as SVG

Required Scopes

Create a Figma personal access token with these scopes:

Scope	Purpose
file_content:read	Read file nodes, layout, styles
library_content:read	Read published components/styles
file_variables:read	Read variables (Enterprise only, optional)

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.

API Calls & Rate Limits

One execution of get_figma_design makes the following API calls:

Call	Endpoint	Tier	Description
1	GET /v1/files/{fileKey}/nodes	T1	Fetch requested nodes (geometry=paths)
2	GET /v1/files/{fileKey}/styles	T3	Fetch all styles
3	GET /v1/files/{fileKey}/variables/local	T2	Fetch local variables (if resolveVariables=true)
4	GET /v1/components/{key}	T3	Resolve component key → library file (up to 3 tries)
5	GET /v1/files/{libFileKey}/components	T3	Fetch all components from library
6+	GET /v1/files/{libFileKey}/nodes	T1	Fetch component definitions from each library

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.