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

New Tools: Add to src/mcp/server.ts in the setupTools() method
New Resources: Add to src/mcp/server.ts in the setupResources() method
New Prompts: Add to src/mcp/server.ts in the setupPrompts() method
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

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

Check the documentation
Search existing issues
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.

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

A Model Context Protocol server that collects, analyzes, and manages AI use case data from various information sources with features for web scraping, data analysis, and trend identification.

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