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.

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.

Configuration

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

| Option | CLI | Env | Default | | -------------------------- | ------------------------ | ------------------------------------------- | --------------- | | Figma API key | --figma-api-key | FIGMA_API_KEY | required | | Figma OAuth token | --figma-oauth-token | FIGMA_OAUTH_TOKEN | unset | | Port | --port | FIGMA_TO_CODE_MCP_PORT or PORT | 3333 | | Host | --host | FIGMA_TO_CODE_MCP_HOST | 127.0.0.1 | | Output format | --json | OUTPUT_FORMAT | yaml | | Skip image tools | --skip-image-downloads | SKIP_IMAGE_DOWNLOADS=true | false | | SVG output dir | --svg-output-dir | FIGMA_SVG_OUTPUT_DIR | temp dir | | Prefetch library variables | --library-file-keys | FIGMA_LIBRARY_VARIABLE_PREFETCH_FILE_KEYS | unset | | Cache path | --library-cache-path | FIGMA_MCP_CACHE_PATH | temp cache file | | Cache TTL | n/a | FIGMA_MCP_CACHE_TTL_MS | 7 days | | Force cache refresh | n/a | FIGMA_MCP_REFRESH_CACHE | off |

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:

| 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.