Design Critique MCP Server

A Model Context Protocol (MCP) server that provides comprehensive visual design analysis and critique capabilities. This server enables AI assistants to analyze design images for composition, color harmony, typography, and accessibility compliance.

Features

• Comprehensive Design Critique: Analyzes overall design quality with actionable recommendations

• Color Analysis: Extracts color palettes, analyzes harmony, and checks contrast ratios

• Composition Analysis: Evaluates visual balance, grid alignment, and layout structure

• Typography Analysis: Assesses font hierarchy, readability, and consistency

• Accessibility Compliance: WCAG 2.1 compliance checking with color blindness simulation

• Multi-Format Support: Works with web, mobile, print, and general design formats

• Real-time Analysis: Fast image processing with optimized algorithms

Related MCP server: RendrKit MCP Server

Prerequisites

• Node.js 18 or later

• npm or yarn package manager

• Canvas dependencies (automatically installed)

Installation

Quick Install for Cursor

Note: After installation, update the file path to your actual installation directory.

Manual Installation

1. Clone the repository:

git clone https://github.com/haasonsaas/design-critique-mcp.git
cd design-critique-mcp

2. Install dependencies:

npm install

3. Build the project:

npm run build

Configuration

Claude Desktop Configuration

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "design-critique": {
      "command": "node",
      "args": ["/path/to/design-critique-mcp/dist/index.js"]
    }
  }
}

Available Tools

critique_design

Provides comprehensive design critique with visual analysis including composition, color harmony, typography, and accessibility.

Parameters:

• image_data (required): Base64 encoded image data

• design_type: Type of design - "web", "mobile", "print", or "general" (default: "web")

• target_audience: Target audience description (optional)

• brand_guidelines: Brand guidelines object (optional)

  • colors: Array of brand colors in hex format

  • fonts: Array of brand font names

Returns: Comprehensive analysis including:

• Overall score (0-100)

• Composition analysis with balance and grid alignment

• Color analysis with harmony score and palette

• Typography assessment with hierarchy and readability scores

• Accessibility report with WCAG compliance

• Actionable recommendations

analyze_color_scheme

Analyzes color palette and harmony in a design.

Parameters:

• image_data (required): Base64 encoded image data

Returns:

• Dominant colors with hex codes

• Color harmony analysis

• Contrast issues

• Color relationships (complementary, analogous, etc.)

analyze_layout

Analyzes visual composition and layout structure.

Parameters:

• image_data (required): Base64 encoded image data

• design_type: Type of design - "web", "mobile", "print", or "general" (default: "web")

Returns:

• Layout score (0-100)

• Balance assessment (symmetrical/asymmetrical)

• Grid alignment detection

• Visual hierarchy analysis

• White space evaluation

analyze_typography

Analyzes typography hierarchy, readability, and font usage.

Parameters:

• image_data (required): Base64 encoded image data

Returns:

• Font count and families detected

• Hierarchy score (0-100)

• Readability score (0-100)

• Typography issues and recommendations

analyze_accessibility

Analyzes design accessibility including contrast, color blindness, and WCAG compliance.

Parameters:

• image_data (required): Base64 encoded image data

Returns:

• Accessibility score (0-100)

• Contrast ratio checks

• Color blindness simulation results

• WCAG compliance issues

• Accessibility recommendations

check_color_contrast

Checks contrast ratio between two specific colors.

Parameters:

• foreground (required): Foreground color in hex format (e.g., "#000000")

• background (required): Background color in hex format (e.g., "#ffffff")

Returns:

• Contrast ratio

• WCAG AA compliance (pass/fail)

• WCAG AAA compliance (pass/fail)

• Recommended use cases

Example Usage

Basic Design Critique

// In Claude Desktop
const result = await critique_design({
  image_data: "base64_encoded_image_data_here",
  design_type: "web",
  target_audience: "Young professionals aged 25-35"
});

Check Specific Color Contrast

const contrast = await check_color_contrast({
  foreground: "#333333",
  background: "#f0f0f0"
});
// Returns: { ratio: 11.2, passes_aa: true, passes_aaa: true }

Analyze Mobile App Design

const analysis = await critique_design({
  image_data: "base64_encoded_image_data",
  design_type: "mobile",
  brand_guidelines: {
    colors: ["#FF5722", "#00BCD4", "#FFC107"],
    fonts: ["Roboto", "Open Sans"]
  }
});

Use Cases

Design Reviews

• Automated design quality assessment

• Consistency checking across design systems

• Pre-launch design validation

Accessibility Auditing

• WCAG compliance verification

• Color contrast validation

• Color blindness impact assessment

Design Education

• Learning design principles through AI feedback

• Understanding composition and color theory

• Improving typography choices

Brand Compliance

• Checking designs against brand guidelines

• Ensuring consistent visual language

• Validating color palette usage

Development

# Run in development mode
npm run dev

# Run tests
npm test

# Lint code
npm run lint

# Type check
npm run typecheck

Architecture

The server uses a modular architecture with specialized analyzers:

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  MCP Client     │────▶│  Design Analysis │────▶│   Analyzers     │
│  (Claude)       │     │     Server       │     │  - Color        │
│                 │◀────│                  │     │  - Composition  │
└─────────────────┘     └──────────────────┘     │  - Typography   │
                                                  │  - Accessibility│
                                                  └─────────────────┘

Image Processing

• Supports common image formats (PNG, JPEG, WebP)

• Automatic image optimization for faster processing

• Maximum image size: 10MB

• Recommended resolution: 1920x1080 for web designs

Performance

• Average analysis time: 2-5 seconds per image

• Concurrent request handling

• Memory-efficient image processing

• Caching for repeated analyses

Troubleshooting

"Invalid image data"

• Ensure the image is properly base64 encoded

• Check that the image format is supported

• Verify the image size is under 10MB

"Canvas not found" error

• Run npm install canvas to install native dependencies

• On macOS: May need to install Cairo graphics library

• On Linux: Install required system dependencies

Analysis taking too long

• Large images may take longer to process

• Consider resizing images before analysis

• Check system resources (CPU/memory)

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Jonathan Haas - GitHub Profile

Acknowledgments

• Built for integration with Anthropic's Claude via MCP

• Uses sharp for image processing

• Powered by chroma-js for color analysis

• Typography detection with Tesseract.js

• Accessibility standards from WCAG 2.1

Support

If you encounter any issues or have questions:

• Open an issue on GitHub Issues

• Review the troubleshooting section above

• Check the MCP documentation