alexandrekumagae/ai-content-categorization-mcp

🎓 MCP Content Curation Server

A Model Context Protocol (MCP) server for intelligent course content curation powered by GPT-4. This server provides AI-driven tools to categorize, tag, and improve educational content.

✨ Features

• 🗂️ Smart Categorization: AI-powered category suggestions for course content
• 🏷️ Intelligent Tagging: Context-aware tag recommendations using GPT-4
• ✨ Content Optimization: Improve titles and descriptions following best practices
• 🔌 MCP Integration: Seamless integration with Claude Desktop and other MCP clients

🚀 Quick Start

Prerequisites

• Node.js 18+
• OpenAI API key

Installation

1. Clone the repository

   git clone https://github.com/yourusername/mcp-content-curation-server.git
   cd mcp-content-curation-server
   

2. Install dependencies

   npm install
   

3. Configure environment

   cp .env.example .env
   # Edit .env and add your OpenAI API key
   

4. Run the server

   # Development mode
   npm run dev
   
   # Production mode
   npm run build
   npm start
   

🔧 OpenAI Setup

1. Get your API key from OpenAI Platform
2. Add it to your .env file:

   OPENAI_API_KEY=sk-your-actual-api-key-here
   

🖥️ Claude Desktop Integration

Update your claude_desktop_config.json:

Development Mode:

{
  "mcpServers": {
    "content-curation": {
      "command": "npx",
      "args": ["tsx", "/path/to/your/project/src/server.ts"],
      "cwd": "/path/to/your/project",
      "env": {
        "NODE_ENV": "development"
      }
    }
  }
}

Production Mode:

{
  "mcpServers": {
    "content-curation": {
      "command": "node",
      "args": ["/path/to/your/project/dist/server.js"],
      "cwd": "/path/to/your/project",
      "env": {
        "NODE_ENV": "production"
      }
    }
  }
}

🛠️ Available Tools

1. suggest_category

Suggests the most appropriate category for course content.

Input:

{
  "title": "Python for Data Science",
  "description": "Learn data analysis with pandas and matplotlib"
}

2. suggest_tags

Recommends relevant tags based on course content.

Input:

{
  "title": "Digital Marketing Fundamentals",
  "description": "Master SEO, Google Ads, and social media marketing"
}

3. improve_content

Optimizes titles and descriptions following educational best practices.

Input:

{
  "title": "JavaScript Basics",
  "description": "Learn programming fundamentals"
}

📊 Data Structure

The server includes:

• 5 main categories: Technology, Business, Design, Marketing, Analytics
• 18 contextual tags: Organized by subject area
• 10 sample courses: For similarity analysis and training

💡 Usage Examples

Categorization

Suggest a category for: "Advanced React Hooks" - "Custom hooks and performance optimization in React"

Tagging

What tags would you recommend for: "Machine Learning with Python"?

Content Improvement

Improve this content:
Title: "Excel Basics"
Description: "Learn spreadsheets"

🛠️ Development

Available Scripts

• npm run dev - Start development server
• npm run build - Compile TypeScript
• npm start - Run production server
• npm run debug - Run diagnostics

Project Structure

src/
├── server.ts              # Main MCP server
├── services/
│   ├── ai.service.ts       # OpenAI GPT-4 integration
│   └── curation.service.ts # Curation logic
├── data/
│   └── mock-data.ts        # Categories, tags, and sample data
└── types.ts               # TypeScript definitions