How the Model Context Protocol (MCP) Bridges AI and Design Data

MCP (Model Context Protocol) Overview

Model Context Protocol (MCP) is an open standard that defines how AI applications interact with external data sources and tools in a secure, controlled manner. Its core idea is to provide a standardized context‑acquisition mechanism so that AI assistants can access real‑time, accurate information to enhance their responses.

MCP Working Principle

MCP is based on a client‑server architecture:

MCP client : usually an AI application such as Cursor or Claude code.

MCP server : a service that provides specific functions or data access.

Transport layer : supports STDIO, HTTP and other communication methods.

MCP borrows micro‑service design ideas; the AI client discovers services and gains security controls. MCP services expose “Tools” via a unified JSON‑RPC interface, embedding a context field (e.g., user intent, history) that allows dynamic strategy adjustment.

Figma Context MCP

Figma Context MCP is a specialized MCP service that solves the problem of AI assistants not being able to directly access Figma design files. It bridges design and development, ensures design consistency, and trims raw Figma data to the parts most useful for code generation.

Project Structure

src/
├── cli.ts            # command‑line entry, supports STDIO and HTTP
├── config.ts         # configuration management
├── mcp.ts            # MCP server entry, tool registration and handling
├── server.ts         # HTTP server implementation
├── services/
│   ├── figma.ts      # Figma API wrapper
│   └── simplify-node-response.ts  # data simplification
├── transformers/
│   ├── layout.ts
│   ├── style.ts
│   └── effects.ts
└── utils/
    ├── common.ts
    ├── logger.ts
    └── sanitization.ts

Core Dependencies

{
  "@modelcontextprotocol/sdk": "^1.10.2",
  "@figma/rest-api-spec": "^0.24.0",
  "zod": "^3.24.2",
  "remeda": "^2.20.1"
}

Run Flow

How to Use

Start the server with a STDIO transport for local debugging:

// launch server
if (import.meta.url === `file://${process.argv[1]}`) {
  const server = createServer({
    figmaApiKey: "figd_your_figma_token",
    figmaOAuthToken: "",
    useOAuth: false,
  });
  try {
    const transport = new StdioServerTransport();
    await server.connect(transport);
    console.error('📱 MCP server started');
  } catch (error) {
    console.error('❌ MCP server start failed:', error);
    process.exit(1);
  }
}

Configure Cursor (or similar AI tool) to load the MCP server:

{
  "figma-mcp": {
    "command": "node",
    "args": ["/Users/maoxiongyu/Code/figma-context-mcp/dist/index.js"]
  }
}

After registration, the AI assistant can discover the get_figma_data tool, call it, and receive the design data as part of its context.

Figma Data Simplification Mechanism

The core simplification logic resides in src/services/simplify-node-response.ts. The entry function parseFigmaResponse:

Handles both GetFileResponse and GetFileNodesResponse.

Aggregates component information.

Filters visible nodes and recursively parses them.

Builds a global style variable system for de‑duplication.

Returns the final simplified design object.

The recursive parseNode function processes each node depth‑first, extracts text styles, fills, strokes, effects, layout, and registers style variables via findOrCreateVar. It also converts absolute pixel values to relative units (e.g., line‑height to em, letter‑spacing to %) to improve responsiveness.

Transformers – Modular Design

Three dedicated transformers handle specific style categories:

Effects Transformer ( src/transformers/effects.ts) – classifies drop and inner shadows and generates CSS box‑shadow or text‑shadow strings.

Style Transformer ( src/transformers/style.ts) – processes border styles, supporting independent stroke weights.

Layout Transformer ( src/transformers/layout.ts) – converts Figma Auto Layout to CSS Flexbox, mapping direction, alignment, spacing, and gaps.

Image Handling

Two strategies are used:

Fill Images – retrieve existing background or logo images via getImageFills.

Rendered Images – render nodes (icons, shapes, text) to PNG or SVG files using getImages.

Both approaches store the resulting files locally for later use by the AI assistant.

Conclusion

With MCP, designers can create in Figma while AI assistants consume the design as structured, executable data. This shifts development from “screenshot‑driven” to “data‑driven”, enabling AI to generate more accurate code and allowing seamless integration across tools.