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AI Use Cases MCP Server

by makinokeiichi
GitHub
TypeScript
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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

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

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security - not tested
F
license - not found
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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.

  1. Features
    1. 🔍 Web Scraping Tools
    2. 📊 Data Analysis Tools
    3. 📈 Resources
    4. 🤖 Prompts
  2. Installation
    1. Usage
      1. As an MCP Server
      2. Available Tools
      3. Available Resources
      4. Available Prompts
    2. Data Structure
      1. AI Use Case
      2. Scraping Configuration
    3. Supported Categories
      1. Development
        1. Project Structure
        2. Building
        3. Adding New Features
      2. Configuration
        1. Database
        2. Scraping Settings
      3. External Dependencies
        1. Security Considerations
          1. Contributing
            1. License
              1. Support

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