AI Use Cases MCP Server

# 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 ```bash # 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: ```json { "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:** ```json { "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 ```typescript 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 ```typescript 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 ```bash # 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 --- **Note**: This server is designed for educational and research purposes. Please respect website terms of service and robots.txt when scraping data.