Artifact Synchronization Utility: Figma to Cursor via MCP

This specialized Model Context Protocol (MCP) module establishes a robust bridge connecting design assets residing in Figma to the intelligent context awareness capabilities within Cursor. Its primary function is the acquisition, refinement, and subsequent structured conversion of visual design data into development-ready code constructs.

Core Capabilities

• Design Data Ingestion: Securely fetch asset blueprints from specified Figma file references (URLs or unique identifiers).
• Payload Compression: Implement aggressive trimming routines to prune extraneous metadata from the resulting JSON structure.
• Style Sheet Generation: Automatically segment and categorize normalized styling properties (e.g., type scales, color palettes, layout rules) into distinct CSS modules.
• Semantic Naming: Programmatically generate descriptive and consistent CSS selector names based on intrinsic property characteristics.
• Tokenization: Isolate and formalize core design tokens, specifically those related to typographic definitions and color schemes.

Deployment Protocol

1. Repository Cloning:

git clone

1. Dependency Resolution:

npm install

1. Configuration Setup: Establish a .env configuration file containing the requisite Figma access credential:

FIGMA_API_KEY=your_figma_api_token_here

1. Service Initiation:

node index.js

Operational Instructions (Within Cursor Chat Interface)

Interactions are facilitated through predefined tool invocations exposed via the Cursor conversational layer:

1. Fetching Figma Artifacts

Fetch design information from Figma reference https://www.figma.com/file/abc123/my-design?node-id=123-456

Output: The MCP server will relay the data payload, pre-processed for efficiency.

2. Extracting Design Primitives (Tokens)

Derive typographic and palette tokens from Figma URL https://www.figma.com/file/abc123/my-design

Output: A structured JSON artifact containing only the derived design tokens.

Illustrative Response Structure:

{ "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. Parameter Modifiers

• Tool: figmaDesign:
• fullJson=true: Suppresses size reduction, returning the raw, voluminous data structure.

• cleanData=true: Activates metadata scrubbing, retaining only properties essential for structural rendering.

• Tool: figmaTokens:

• tokenTypes=["typography"]: Constrains output exclusively to type definitions.
• tokenTypes=["colors"]: Limits output exclusively to color values.
• tokenTypes=["typography", "colors"]: The default setting, encompassing both major token categories.

Invocation Example (Colors Only):

Extract exclusively color variables from Figma document https://www.figma.com/file/abc123/my-design

Internal Data Mapping

The resulting representation of the processed Figma input adheres to this schema:

{ "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 Specifics

Design Asset Fetching

Leverages the official Figma HTTP endpoint interface. Compatibility extends to both legacy and contemporary Figma URL formats. Node specification via the node-id parameter is supported.

Data Stream Refinement

The process for achieving optimal data density involves: 1. Filtering out non-essential attributes. 2. Partitioning resultant styles into semantic buckets (e.g., type, hue, positioning). 3. Consolidating functionally identical style definitions to minimize redundancy. 4. Automated synthesis of corresponding CSS class identifiers.

Token Derivation Logic

Token generation follows these stages: 1. Identification of utilized typographic and hue attributes within the design tree. 2. Establishment of a uniform, prescribed nomenclature for every abstracted token. 3. Categorization of tokens based on their type (typographic vs. chromatic). 4. Deduplication and arrangement for streamlined integration into established design language systems.

Constraints & Caveats

• MCP data transfer limits may necessitate persistence of extensive datasets to the local figma_data storage area.
• Intricate geometrical descriptions within vector objects might suffer fidelity loss during abstraction.
• Advanced rendering effects, such as complex color transitions or shadows, may necessitate downstream manual refinement.

Licensing

This project operates under the terms of the MIT License.