nguyenlegravityglobal figma mcp

nguyenlegravityglobal figma mcp avatar

by nguyenlegravityglobal

What is nguyenlegravityglobal figma mcp

Gravity Global Figma MCP

Gravity Global Figma MCP is a tool that integrates Figma with Cursor through the Model Context Protocol (MCP), allowing you to retrieve and optimize design data from Figma for code conversion.

Key Features

  • Figma Data Retrieval: Get design information from Figma files using URLs or file IDs
  • Data Optimization: Reduce JSON size by removing unnecessary properties
  • CSS Conversion: Automatically divide and organize styles into groups (typography, colors, layouts...)
  • CSS Class Name Generation: Automatically create meaningful class names based on properties
  • Design Tokens Extraction: Extract typography and color tokens from Figma designs

Installation

  1. Clone the repository:
git clone <repository-url>
  1. Install dependencies:
npm install
  1. Create a .env file and add your Figma API token:
FIGMA_API_KEY=your_figma_api_token_here
  1. Run the MCP server:
node index.js

Usage

In Cursor Chat

You can use the following tools through Cursor Chat:

1. Retrieve Data from Figma

Get data from Figma URL https://www.figma.com/file/abc123/my-design?node-id=123-456

Result: The MCP will return optimized Figma data.

2. Extract Design Tokens

Extract typography and color tokens from Figma URL https://www.figma.com/file/abc123/my-design

Result: The MCP will return a JSON object containing typography and color tokens extracted from the Figma design.

Example response:

{
  "typography": {
    "opensans-600-32": {
      "fontFamily": "Open Sans",
      "fontSize": "32px",
      "fontWeight": 600,
      "lineHeight": "48px"
    },
    "avenirnext-400-16": {
      "fontFamily": "Avenir Next",
      "fontSize": "16px",
      "fontWeight": 400,
      "lineHeight": "24px"
    }
  },
  "colors": {
    "bg-ffffff": "#ffffff",
    "text-030e12": "#030e12"
  }
}

3. Options

  • figmaDesign tool:

    • fullJson=true: Returns full uncompressed JSON data
    • cleanData=true: Removes unnecessary properties for HTML/CSS rendering
  • figmaTokens tool:

    • tokenTypes=["typography"]: Only extract typography tokens
    • tokenTypes=["colors"]: Only extract color tokens
    • tokenTypes=["typography", "colors"]: Extract both (default)

Example:

Extract only color tokens from Figma URL https://www.figma.com/file/abc123/my-design

Data Structure

The converted Figma data has the following structure:

{
  "nodes": {
    "nodeId": {
      "id": "nodeId",
      "name": "Node Name",
      "type": "FRAME",
      "fillStyleId": "style123",
      "layoutStyleId": "style456",
      "children": [...]
    }
  },
  "styles": {
    "style123": {
      "backgroundColor": "#ffffff",
      "opacity": 1,
      "categories": { "colors": "color1" }
    }
  },
  "optimizedStyles": {
    "typography": {...},
    "colors": {...},
    "layout": {...},
    "spacing": {...},
    "sizing": {...}
  },
  "classNames": {
    "style123": "bg-1",
    "style456": "flex-row-1"
  }
}

Technical Details

Figma Data Retrieval

The tool uses the Figma REST API to fetch design data. It supports URLs from both regular files and new design URLs. You can specify a particular node using node-id.

Data Optimization

The optimization process includes these steps:

  1. Removing unnecessary properties
  2. Dividing styles into groups (typography, colors, layout...)
  3. Merging similar styles to reduce duplication
  4. Automatically generating CSS class names

Token Extraction

The token extraction process:

  1. Identifies typography and color styles used in the design
  2. Creates standardized naming conventions for each token
  3. Groups tokens by type (typography or colors)
  4. Removes duplicates and organizes them for easy integration with design systems

Limitations

  • MCP has limitations on the size of returned data; large files will be saved to the figma_data directory
  • Complex vector properties are not fully preserved
  • Complex gradients and effects may require additional processing

License

MIT License

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.

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