Salute: An AI design system generator that generates as you code.

AI-Powered Design System Generator Specification

Executive Summary

This specification outlines the architecture and implementation of an innovative AI-powered design system generator that bridges the gap between design and engineering workflows. The system's primary focus is enabling engineers to generate comprehensive design system documentation as they build, creating a truly unified design-to-code workflow delivered through Model Context Protocol (MCP) servers. ^[emkvx2] ^[chi5g7]

Vision & Goals

Primary Objectives

Engineer-First Documentation: Enable engineers to generate design system documentation directly from their code implementations^[67aeae] ^[wdm323]
Bidirectional Synchronization: Maintain real-time sync between design tools and code repositories^[urqh0r] ^[nd2bbt]
AI-Powered Automation: Leverage LLMs to translate between design specifications and implementable components^[obbkg0] ^[2wmeyb]
Unified Workflow: Create a seamless bridge between UI design and engineering processes^[rcg3af]

Key Differentiators

Documentation generation happens during development, not after
Engineers become active contributors to design system documentation
AI assists in maintaining consistency across design and code^[3iskas] ^[2wmeyb]

Component Display Component Specification

Overview

A reusable component for displaying and documenting UI components in a design system. This component provides a clean, interactive way to showcase components with code examples, variants, and usage guidelines.

Features

1. Component Preview

Displays the rendered component in an isolated container
Responsive preview area with viewport size controls
Toggle between light/dark mode
Background color customization
Toggle component states/interactions

2. Code Display

Syntax-highlighted code examples
Toggle between different frameworks (React, Vue, Svelte, etc.)
Copy to clipboard functionality
Expandable/collapsible code blocks

3. Component Controls

Interactive prop controls (sliders, toggles, selects)
Live preview updates as props change
Preset configurations
Reset to defaults option

4. Documentation

Component name and description
Status badges (New, Deprecated, Experimental)
Version information
Last updated timestamp
Dependencies

Props

Prop	Type	Default	Description
`title`	string	''	The name of the component
`description`	string	''	Brief description of the component
`component`	React/Vue/Svelte Component	-	The actual component to display
`variants`	Array<{name: string, props: object}>	[]	Different variants of the component
`code`	string	''	Example code for the component
`status`	'stable' \| 'beta' \| 'deprecated'	'stable'	Component status
`version`	string	'1.0.0'	Component version
`dependencies`	string[]	[]	List of dependencies

Usage Example

jsx

<ComponentDisplay
  title="Button"
  description="A simple button component with multiple variants and sizes."
  component={Button}
  variants={[
    { name: 'Primary', props: { variant: 'primary', children: 'Click Me' } },
    { name: 'Secondary', props: { variant: 'secondary', children: 'Click Me' } },
  ]}
  code={`<Button variant="primary">Click Me</Button>`}
  status="stable"
  version="1.2.0"
  dependencies={['@your-design-system/core']}
/>

Accessibility

Keyboard navigation support
Screen reader friendly
Proper ARIA attributes
Focus management

Responsive Behavior

Adapts to different screen sizes
Mobile-friendly controls
Horizontal scrolling for wide components
Toggleable device frames

Theme Support

Light/dark mode
Custom theming
Contrast ratio checking
Color blindness simulation

Development Guidelines

Use TypeScript for type safety
Follow WCAG 2.1 AA standards
Document all props and methods
Include unit tests
Add storybook stories
Support SSR/SSG

Future Enhancements

Interactive playground
Visual regression testing
Performance metrics
Bundle size analysis
Automated screenshot testing

System Architecture

Core Components

1. MCP Server Architecture

The system implements a multi-server MCP architecture^[japg2p] [12]:

text

┌─────────────────────────────────────────────────┐
│                  MCP Host                       │
│         (IDE/Development Environment)           │
├─────────────────────────────────────────────────┤
│  MCP Client 1    │  MCP Client 2   │ MCP Client 3│
└──────┬───────────┴──────┬──────────┴─────┬──────┘
       │                  │                 │
       ▼                  ▼                 ▼
┌──────────────┐  ┌──────────────┐  ┌──────────────┐
│Design System │  │Documentation │  │   AI Agent   │
│   Server     │  │    Server    │  │    Server    │
└──────────────┘  └──────────────┘  └──────────────┘

2. AI Orchestration Framework

Built on LangChain/LangGraph for sophisticated agent workflows^[fc33aj] ^[5m26pl] [15]:

Multi-Agent Architecture: Specialized agents for different tasks (design analysis, code generation, documentation writing)
Context Engineering: Full control over prompts and agent interactions^[j9qfej]
Human-in-the-Loop: Approval workflows for critical changes^[b1gp81]

Technical Stack

Backend Infrastructure

Runtime: Node.js/TypeScript for MCP server implementation^[4655sr]
AI Framework: LangChain/LangGraph for orchestration^[fc33aj] ^[0pdc5h] ^[b1gp81]
API Layer: GraphQL with real-time subscriptions^[0m4vgj]
Database: PostgreSQL with vector extensions for semantic search
Message Queue: Redis for inter-service communication

Frontend Tools

Design Integration: Figma API for design token extraction^[xjts7a] ^[afj9nm]
Documentation Engine: MDX-based system with live component rendering^[u4os0c]
Code Generation: Style Dictionary for multi-platform token transformation^[afj9nm] ^[a6fbdo]

AI/ML Components

LLMs: Support for multiple providers (OpenAI, Anthropic, local models)
Embeddings: Vector database for semantic component search
Fine-tuning: Custom models for organization-specific patterns

Key Features

1. Automated Design Token Management

The system automatically extracts and manages design tokens^[7qig1h] [25]:

typescript

interface TokenWorkflow {
  extract: {
    source: 'Figma' | 'Code' | 'Documentation';
    format: 'Variables' | 'Styles' | 'Components';
  };
  transform: {
    target: 'CSS' | 'Swift' | 'Android' | 'React';
    method: 'StyleDictionary' | 'Custom';
  };
  sync: {
    direction: 'Bidirectional' | 'DesignToCode' | 'CodeToDesign';
    trigger: 'Realtime' | 'Commit' | 'Manual';
  };
}

2. Engineer-Driven Documentation Generation

Engineers can generate documentation through:

Code Comments & Annotations

typescript

/**
 * @designToken primary-button
 * @category buttons
 * @status stable
 * @accessibility WCAG AA compliant
 */
export const PrimaryButton = styled.button`
  background: var(--color-primary);
  // AI automatically generates usage docs from implementation
`;

MCP Commands

bash

# Generate component documentation
mcp generate-docs --component PrimaryButton

# Update design system from code changes
mcp sync-tokens --source code --target figma

3. Real-Time Design-Code Synchronization

Implements bidirectional sync using^[urqh0r] [6]:

Figma Webhooks: Instant updates when designs change
Git Hooks: Automatic documentation updates on commits
WebSocket Connections: Live preview during development

4. AI-Powered Component Translation

The AI agent can^[obbkg0] ^[3jh7m9] [8]:

Convert Figma components to framework-specific code
Generate responsive variations automatically
Create accessibility-compliant implementations
Suggest design token optimizations

MCP Server Implementation

Server Types

1. Design System Server

Handles design token management and component registry:

typescript

interface DesignSystemServer {
  resources: {
    '/tokens': TokenResource;
    '/components': ComponentResource;
    '/themes': ThemeResource;
  };
  tools: {
    'extract-tokens': ExtractTokensTool;
    'validate-tokens': ValidateTokensTool;
    'generate-theme': GenerateThemeTool;
  };
  prompts: {
    'component-spec': ComponentSpecPrompt;
    'token-naming': TokenNamingPrompt;
  };
}

2. Documentation Server

Manages documentation generation and updates^[67aeae] [27]:

typescript

interface DocumentationServer {
  tools: {
    'generate-docs': GenerateDocsTool;
    'update-docs': UpdateDocsTool;
    'validate-docs': ValidateDocsTool;
  };
  resources: {
    '/templates': DocTemplateResource;
    '/examples': CodeExampleResource;
  };
}

3. AI Agent Server

Orchestrates LLM interactions^[5m26pl] [16]:

typescript

interface AIAgentServer {
  tools: {
    'analyze-design': AnalyzeDesignTool;
    'generate-code': GenerateCodeTool;
    'suggest-improvements': SuggestImprovementsTool;
  };
  prompts: {
    'design-to-code': DesignToCodePrompt;
    'code-review': CodeReviewPrompt;
  };
}

Workflow Examples

1. Component Creation Workflow

graph LR A[Engineer creates component] --> B[AI analyzes implementation] B --> C[Generate design tokens] C --> D[Create documentation] D --> E[Sync to Figma] E --> F[Update style guide]

2. Design Update Workflow

graph LR A[Designer updates Figma] --> B[MCP receives webhook] B --> C[AI analyzes changes] C --> D[Generate code updates] D --> E[Create PR with changes] E --> F[Engineer reviews|approves]

Security & Governance

Access Control

Role-based permissions for design system modifications
Audit trails for all automated changes
Approval workflows for breaking changes

Data Security

End-to-end encryption for design assets
Secure token storage with environment-specific access
GDPR-compliant data handling

Performance Considerations

Scalability

Microservice architecture for independent scaling^[japg2p]
Caching layers for frequently accessed tokens^[0m4vgj]
CDN distribution for generated assets

Optimization

Incremental updates to minimize processing
Batch operations for bulk changes
Lazy loading for documentation sites

Integration Points

Development Tools

VS Code Extension: Direct IDE integration^[emkvx2] ^[rcg3af]
CI/CD Pipelines: Automated validation and deployment^[5ap3yg]
Git Workflows: Branch-specific design systems

Design Tools

Figma Plugin: Two-way sync capabilities^[xjts7a] ^[rcg3af]
Sketch Integration: Token import/export
Adobe XD Support: Component mapping

Documentation Platforms

Storybook Integration: Auto-generated stories^[u4os0c]
ZeroHeight Sync: Documentation updates^[u4os0c]
Custom Portals: API-driven content

Success Metrics

Quantitative Metrics

50% reduction in design-to-code time^[urqh0r]
90% consistency in component implementations
75% reduction in documentation maintenance effort

Qualitative Metrics

Improved designer-developer collaboration
Higher design system adoption rates
Better component reusability

Implementation Roadmap

Phase 1: Foundation (Months 1-3)

Core MCP server architecture
Basic token extraction and sync
MVP documentation generation

Phase 2: AI Integration (Months 4-6)

LangChain integration
Component translation capabilities
Automated documentation writing

Phase 3: Advanced Features (Months 7-9)

Multi-framework support
Advanced AI suggestions
Performance optimizations

Phase 4: Enterprise Features (Months 10-12)

Advanced governance tools
Analytics and insights
Custom AI model training

Conclusion

This AI-powered design system generator represents a paradigm shift in how teams approach design system documentation and maintenance. By placing engineers at the center of the documentation process and leveraging AI to bridge the design-code gap, we can create more consistent, maintainable, and scalable design systems. ^[obbkg0] ^[67aeae] ^[rcg3af]

The MCP server architecture provides the flexibility and extensibility needed to integrate with existing tools while the AI orchestration layer ensures intelligent automation that enhances rather than replaces human creativity and decision-making. ^[emkvx2] ^[b1gp81] ^[j9qfej]