AI Use Cases MCP Server

A Model Context Protocol (MCP) server for collecting, analyzing, and managing AI use case data from various information sources.

Features

🔍 Web Scraping Tools

• URL Scraping: Extract AI use case data from specified URLs with customizable selectors
• Information Source Management: Add, edit, and manage scraping configurations for different websites
• Automatic Data Extraction: Extract titles, summaries, categories, and publication dates

📊 Data Analysis Tools

• Keyword Extraction: Automatically extract relevant AI and technology keywords from content
• Use Case Categorization: Automatically categorize AI use cases by technology and industry
• Search & Filter: Search through collected use cases with various filters

📈 Resources

• AI Use Cases Data: Access structured AI use case data by category and limit
• Statistics: Get overview statistics of collected data including categories and industries

🤖 Prompts

• Summarize Use Cases: Create concise summaries of AI use cases
• Suggest Sources: Get recommendations for new information sources
• Analyze Trends: Analyze trends in AI use cases over time

Installation

# Clone the repository
git clone <repository-url>
cd ai-use-cases-mcp-server

# Install dependencies
npm install

# Build the project
npm run build

# Start the server
npm start

Usage

As an MCP Server

The server can be used with any MCP-compatible client (Claude Desktop, Cursor, etc.) by adding it to your MCP configuration:

{
  "mcpServers": {
    "ai-use-cases": {
      "command": "node",
      "args": ["dist/index.js"]
    }
  }
}

Available Tools

1. Web Scraping Tool (scrape-url)

Scrape AI use case data from a specified URL.

Parameters:

• url (string): The URL to scrape
• selectors (object, optional): CSS selectors for data extraction
• extractKeywords (boolean, default: true): Whether to extract keywords

Example:

{
  "url": "https://example.com/ai-use-case",
  "selectors": {
    "title": "h1.title",
    "summary": ".content p",
    "category": ".category"
  }
}

2. Add Information Source (add-source)

Add a new information source for automated data collection.

Parameters:

• name (string): Source name
• url (string): Source URL
• selectors (object): CSS selectors for data extraction

3. Search Use Cases (search-use-cases)

Search through collected AI use cases with filters.

Parameters:

• query (string): Search query
• category (string, optional): Filter by category
• industry (string, optional): Filter by industry
• technology (string, optional): Filter by technology
• limit (number, default: 20): Maximum results

4. Extract Keywords (extract-keywords)

Extract relevant keywords from text content.

Parameters:

• text (string): Text to analyze
• maxKeywords (number, default: 10): Maximum keywords to extract
• category (string, optional): Category for context-specific extraction

5. Categorize Use Case (categorize-use-case)

Automatically categorize an AI use case based on its content.

Parameters:

• title (string): Use case title
• summary (string): Use case summary
• content (string, optional): Additional content

Available Resources

1. AI Use Cases (ai-use-cases://{category}/{limit})

Access AI use case data by category and limit.

Parameters:

• category (string): Category filter (optional)
• limit (number): Maximum number of results

2. Statistics (statistics://overview)

Get overview statistics of collected data.

Available Prompts

1. Summarize Use Case (summarize-use-case)

Create a concise summary of an AI use case.

Parameters:

• title (string): Use case title
• content (string): Use case content
• maxLength (number, default: 200): Maximum summary length

2. Suggest Sources (suggest-sources)

Get recommendations for new information sources.

Parameters:

• industry (string, optional): Target industry
• technology (string, optional): Target technology
• category (string, optional): Target category

3. Analyze Trends (analyze-trends)

Analyze trends in AI use cases over time.

Parameters:

• timeframe (string): Analysis timeframe
• category (string, optional): Category filter
• industry (string, optional): Industry filter

Data Structure

AI Use Case

interface AIUseCase {
  id: string;
  title: string;
  summary: string;
  sourceUrl: string;
  category: string;
  industry?: string;
  technologyKeywords: string[];
  publicationDate?: string;
  company?: string;
  implementationDetails?: string;
  results?: string;
  createdAt: string;
  updatedAt: string;
}

Scraping Configuration

interface ScrapingConfig {
  id: string;
  name: string;
  url: string;
  selectors: {
    title: string;
    summary: string;
    date?: string;
    author?: string;
    category?: string;
  };
  enabled: boolean;
  lastScraped?: string;
  createdAt: string;
  updatedAt: string;
}

Supported Categories

The system automatically categorizes AI use cases into the following categories:

• Natural Language Processing: NLP, text analysis, chatbots, translation
• Computer Vision: Image recognition, video analysis, object detection
• Machine Learning: Algorithms, predictive analytics, classification
• Robotics & Automation: Industrial automation, autonomous systems
• Data Analytics: Business intelligence, reporting, visualization
• Healthcare & Medical: Medical diagnosis, drug discovery, telemedicine
• Finance & Banking: Fraud detection, risk assessment, trading
• E-commerce & Retail: Recommendation systems, personalization
• Transportation & Logistics: Route optimization, autonomous vehicles
• Education & Training: Personalized learning, adaptive systems

Development

Project Structure

src/
├── types/           # TypeScript type definitions
├── database/        # Database layer (SQLite)
├── scraping/        # Web scraping functionality
├── analysis/        # Text analysis and keyword extraction
├── mcp/            # MCP server implementation
└── index.ts        # Main entry point

Building

# Development
npm run dev

# Build for production
npm run build

# Run tests
npm test

# Lint code
npm run lint

# Format code
npm run format

Adding New Features

1. New Tools: Add to src/mcp/server.ts in the setupTools() method
2. New Resources: Add to src/mcp/server.ts in the setupResources() method
3. New Prompts: Add to src/mcp/server.ts in the setupPrompts() method
4. New Types: Add to src/types/index.ts

Configuration

Database

The server uses SQLite for data storage. The database file (ai_use_cases.db) is created automatically in the project root.

Scraping Settings

• Default timeout: 10 seconds
• Retry attempts: 3
• Delay between requests: 1 second
• User agent: Standard browser user agent

External Dependencies

• @modelcontextprotocol/sdk: MCP protocol implementation
• axios: HTTP client for web scraping
• cheerio: HTML parsing
• puppeteer: Headless browser for JavaScript-heavy pages
• sqlite3: Database storage
• natural: Natural language processing for keyword extraction
• zod: Schema validation

Security Considerations

• The server includes DNS rebinding protection for HTTP transport
• All user inputs are validated using Zod schemas
• Web scraping respects robots.txt and includes delays between requests
• No sensitive data is stored or transmitted

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

For issues and questions:

1. Check the documentation
2. Search existing issues
3. Create a new issue with detailed information

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Note: This server is designed for educational and research purposes. Please respect website terms of service and robots.txt when scraping data.