SuperComponents Server

MCP server for AI-powered component generation - analyze designs, generate components, and create implementation instructions.

Overview

SuperComponents Server is an MCP (Model Context Protocol) server that provides AI-powered component generation capabilities. It enables developers to parse design inputs, analyze component libraries, and generate implementation instructions through a standardized protocol interface.

Related MCP server: Universal Netlist MCP Server

Installation

Install the Package

# Install in your project
npm install supercomponents-server

# Or install globally (requires additional setup)
npm install -g supercomponents-server

Configure MCP Client

This package is an MCP server that needs to be configured in your MCP client (like Cursor, Claude Desktop, etc.).

For Cursor IDE

Add to your .cursor/mcp.json file:

{
  "mcpServers": {
    "supercomponents-server": {
      "command": "node",
      "args": ["./node_modules/supercomponents-server/build/index.js"]
    }
  }
}

For Claude Desktop

Add to your MCP configuration:

{
  "mcpServers": {
    "supercomponents-server": {
      "command": "node",
      "args": ["path/to/node_modules/supercomponents-server/build/index.js"]
    }
  }
}

Alternative: Using npx

You can also use npx to run the server:

{
  "mcpServers": {
    "supercomponents-server": {
      "command": "npx",
      "args": ["supercomponents-server"]
    }
  }
}

Usage

Once configured, the MCP server provides these tools to your AI assistant:

Available MCP Tools

• parse.design: Convert design inputs into structured JSON representations

• analyze.components: Analyze existing component structures and patterns

• generate.instruction: Generate detailed implementation instructions for component development

Example Usage

The AI assistant can use these tools to:

# Parse a design file
parse.design({ input: "design-file.json" })

# Analyze component library
analyze.components({ library: "react-components" })

# Generate implementation instructions
generate.instruction({ component: "Button", requirements: "..." })

Configuration

Environment Variables

The server may require certain environment variables for AI model integration:

# Example - check specific AI provider requirements
OPENAI_API_KEY=your_api_key_here

Project Structure

When using the server, ensure your project has the expected structure for component analysis:

your-project/
├── src/
│   ├── components/
│   ├── designs/
│   └── ...
├── package.json
└── ...

Development

If you want to contribute to or modify the server:

Setup

# Clone the repository
git clone https://github.com/SuperComponents/SuperComponents.git
cd SuperComponents

# Install dependencies
npm install

# Build the project
npm run build

Development Commands

# Watch mode compilation
npm run dev

# Build for production
npm run build

# Run tests
npm test

# Start the server
npm start

Features

• Design Parsing: Convert design inputs into structured JSON representations

• Component Analysis: Analyze existing component structures and patterns

• Instruction Generation: Generate detailed implementation instructions for component development

• MCP Protocol: Standardized interface for AI assistant integration

• TypeScript Support: Full TypeScript support with type definitions

Architecture

Built with:

• Node.js & TypeScript

• MCP (Model Context Protocol) SDK

• Zod for schema validation

• AI model integration for processing tasks

Troubleshooting

Common Issues

1. MCP Server Not Found: Ensure the package is installed and the path in your MCP configuration points to node_modules/supercomponents-server/build/index.js

2. Permission Errors: Ensure Node.js has permission to execute the server files

3. Port Conflicts: The server uses standard MCP protocol - ensure no conflicts with other MCP servers

4. Module Not Found: Run npm install to ensure all dependencies are installed

Debug Mode

Enable debug logging by setting:

DEBUG=supercomponents-server node node_modules/supercomponents-server/build/index.js

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests if applicable

5. Submit a pull request

License

MIT License - see LICENSE file for details

Support

• GitHub Issues: Report bugs or request features

• Documentation: Full documentation