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Link Scan MCP Server

by chweyun
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Python
Hybrid

Link Scan MCP Server πŸš€

링크λ₯Ό μŠ€μΊ”ν•˜κ³  μš”μ•½μ„ μ œκ³΅ν•˜λŠ” 포괄적인 Model Context Protocol (MCP) μ„œλ²„μž…λ‹ˆλ‹€. YouTube, Instagram Reels λ“± λΉ„λ””μ˜€ 링크와 λΈ”λ‘œκ·Έ, 기사 λ“± ν…μŠ€νŠΈ 링크λ₯Ό μžλ™μœΌλ‘œ κ°μ§€ν•˜κ³  λΆ„μ„ν•˜μ—¬ 3λ¬Έμž₯ μ΄λ‚΄μ˜ κ°„κ²°ν•œ μš”μ•½μ„ μ œκ³΅ν•©λ‹ˆλ‹€. API ν‚€ 없이 λͺ¨λ“  κΈ°λŠ₯을 μ‚¬μš©ν•  수 μžˆμŠ΅λ‹ˆλ‹€!

Link Scan MCP Server - A comprehensive Model Context Protocol (MCP) server for scanning and summarizing links. Automatically detects and analyzes video links (YouTube, Instagram Reels) and text links (blogs, articles) to provide concise 3-sentence summaries. All features work without requiring API keys!

Python 3.11+ | MCP Compatible | License: MIT

✨ Features

πŸŽ₯ Video Link Analysis

  • YouTube Support

    • Comprehensive metadata extraction (title, description)

    • Subtitle extraction for first 7 seconds (yt-dlp)

    • Audio transcription using OpenAI Whisper

    • Integrated summarization combining all text sources

  • Instagram Reels Support

    • Audio download and transcription (first 7 seconds)

    • Automatic content summarization

  • Smart Link Detection

    • Automatic video/text link type detection

    • Error handling for unsupported URLs

πŸ“ Text Link Analysis

  • Web Content Extraction

    • BeautifulSoup-based HTML parsing

    • Main content area detection

    • Automatic navigation/ad removal

  • Intelligent Summarization

    • Llama3-powered text summarization

    • 3-sentence limit enforcement

    • Natural Korean output

πŸ€– AI-Powered Summarization

  • Llama3 Integration

    • Local LLM via Ollama (no API keys required)

    • Separate prompts for video and text content

    • Fallback to original text on errors

  • Whisper Transcription

    • High-quality speech-to-text conversion

    • Optimized for speed and accuracy

    • Supports multiple languages

🐳 Docker Support

  • One-Command Setup

    • Docker Compose configuration

    • Automatic Ollama service setup

    • Llama3 model auto-download

    • Development mode with hot reload

πŸ”§ Developer-Friendly

  • Type-safe with Pydantic models

  • Async/await support for better performance

  • Comprehensive error handling

  • Extensible architecture

  • Hot reload in development mode

πŸš€ Quick Start

Installation

# Clone the repository git clone https://github.com/your-username/mcp-link-scan.git cd mcp-link-scan # Install dependencies pip install -r requirements.txt

System Dependencies

ffmpeg (required for audio processing):

Ollama (required for summarization):

Configuration

Create a .env file:

# μ„œλ²„ μ„€μ • PORT=8000 # μ„œλ²„ 포트 (κΈ°λ³Έκ°’: 8000) HOST=0.0.0.0 # μ„œλ²„ 호슀트 (κΈ°λ³Έκ°’: 0.0.0.0) DEBUG=False # 디버그 λͺ¨λ“œ (κΈ°λ³Έκ°’: False) # API 경둜 prefix (선택) # 같은 μ„œλ²„μ— μ—¬λŸ¬ MCP μ„œλ²„λ₯Ό ν˜ΈμŠ€νŒ…ν•  λ•Œ μ‚¬μš© # κΈ°λ³Έκ°’: /link-scan API_PREFIX=/link-scan # Ollama μ„€μ • (선택) # Docker Composeλ₯Ό μ‚¬μš©ν•˜λŠ” 경우 μžλ™μœΌλ‘œ 섀정됨 OLLAMA_API_URL=http://localhost:11434 # Ollama API URL (κΈ°λ³Έκ°’: http://localhost:11434) OLLAMA_MODEL=llama3:latest # μ‚¬μš©ν•  Ollama λͺ¨λΈ (κΈ°λ³Έκ°’: llama3)

ν™˜κ²½ λ³€μˆ˜ μ„€λͺ…

λ³€μˆ˜λͺ…

ν•„μˆ˜

κΈ°λ³Έκ°’

μ„€λͺ…

PORT

❌

8000

μ„œλ²„κ°€ μ‚¬μš©ν•  포트 번호

HOST

❌

0.0.0.0

μ„œλ²„κ°€ 바인딩할 호슀트 μ£Όμ†Œ

DEBUG

❌

False

디버그 λͺ¨λ“œ ν™œμ„±ν™” (True/False)

API_PREFIX

❌

/link-scan

API μ—”λ“œν¬μΈνŠΈ 경둜 prefix

OLLAMA_API_URL

❌

http://localhost:11434

Ollama API μ„œλ²„ URL

OLLAMA_MODEL

❌

llama3

μ‚¬μš©ν•  Ollama λͺ¨λΈ 이름

Running as MCP Server

Local Mode (stdio):

python -m src.server

Remote Mode (HTTP):

python run_server.py

Or with uvicorn directly:

uvicorn src.server_http:app --host 0.0.0.0 --port 8000

Docker Setup (Recommended)

Using Docker Compose:

# Start all services (link-scan + Ollama) docker-compose up -d # Check logs docker-compose logs -f # Stop services docker-compose down

Docker Compose automatically:

  • Sets up Ollama service with 8GB memory

  • Downloads Llama3 model

  • Configures link-scan service

  • Enables development mode with hot reload

Development Mode: The docker-compose.yml is configured for development with:

  • Source code volume mounting

  • Hot reload enabled (DEBUG=True)

  • Automatic code changes detection

Testing with MCP Inspector

You can test the server using the MCP Inspector tool:

# Test with Python npx @modelcontextprotocol/inspector python run_server.py # Or test stdio mode npx @modelcontextprotocol/inspector python -m src.server

The MCP Inspector provides a web interface to:

  • View available tools and their schemas

  • Test tool execution with sample inputs

  • Debug server responses and error handling

  • Validate MCP protocol compliance

πŸ› οΈ Available Tools

1. scan_video_link

Scan and summarize video links (YouTube, Instagram Reels, etc.).

Parameters:

  • url (string, required): Video URL to scan

Example:

{ "name": "scan_video_link", "arguments": { "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" } }

Process:

  1. Detects link type (YouTube, Instagram, etc.)

  2. For YouTube: Extracts title, description, subtitles (first 7s)

  3. Downloads audio (first 7 seconds)

  4. Transcribes audio with Whisper

  5. Combines all text sources

  6. Summarizes with Llama3 (3 sentences max)

2. scan_text_link

Scan and summarize text links (blogs, articles, etc.).

Parameters:

  • url (string, required): Text URL to scan

Example:

{ "name": "scan_text_link", "arguments": { "url": "https://example.com/blog/article" } }

Process:

  1. Fetches HTML content

  2. Extracts main text content

  3. Removes navigation, ads, and noise

  4. Summarizes with Llama3 (3 sentences max)

πŸ“Š Example Outputs

Video Link Summary

Input: YouTube video URL

Output:

이 μ˜μƒμ€ Python ν”„λ‘œκ·Έλž˜λ° μ–Έμ–΄μ˜ κΈ°λ³Έ κ°œλ…μ„ μ†Œκ°œν•©λ‹ˆλ‹€. λ³€μˆ˜, ν•¨μˆ˜, 클래슀 λ“± 핡심 문법을 μ‹€μŠ΅ μ˜ˆμ œμ™€ ν•¨κ»˜ μ„€λͺ…ν•©λ‹ˆλ‹€. μ΄ˆλ³΄μžλ„ μ‰½κ²Œ 따라할 수 μžˆλ„λ‘ λ‹¨κ³„λ³„λ‘œ κ΅¬μ„±λ˜μ–΄ μžˆμŠ΅λ‹ˆλ‹€.

Text Link Summary

Input: Blog article URL

Output:

이 글은 Docker μ»¨ν…Œμ΄λ„ˆ 기술의 μž₯단점을 λΆ„μ„ν•©λ‹ˆλ‹€. 가상화 기술과 λΉ„κ΅ν•˜μ—¬ λ¦¬μ†ŒμŠ€ νš¨μœ¨μ„±κ³Ό 배포 νŽΈμ˜μ„±μ„ κ°•μ μœΌλ‘œ μ œμ‹œν•©λ‹ˆλ‹€. λ‹€λ§Œ λ³΄μ•ˆκ³Ό λ³΅μž‘μ„± μΈ‘λ©΄μ—μ„œ μ£Όμ˜κ°€ ν•„μš”ν•˜λ‹€κ³  μ‘°μ–Έν•©λ‹ˆλ‹€.

πŸ—οΈ Architecture

mcp-link-scan/ β”œβ”€β”€ src/ β”‚ β”œβ”€β”€ server.py # Local server (stdio) β”‚ β”œβ”€β”€ server_http.py # Remote server (HTTP) β”‚ β”œβ”€β”€ tools/ # MCP tools β”‚ β”‚ β”œβ”€β”€ link_scanner.py # Main tool definitions β”‚ β”‚ β”œβ”€β”€ media_handler.py # Video processing (Whisper) β”‚ β”‚ └── text_handler.py # Text extraction β”‚ β”œβ”€β”€ utils/ # Utilities β”‚ β”‚ β”œβ”€β”€ link_detector.py # Link type detection β”‚ β”‚ β”œβ”€β”€ youtube_extractor.py # YouTube metadata/subtitles β”‚ β”‚ └── llm_summarizer.py # Llama3 integration β”‚ └── prompts/ # LLM prompts β”‚ └── __init__.py # Video/text prompt templates β”œβ”€β”€ docker/ β”‚ └── init-ollama.sh # Ollama initialization script β”œβ”€β”€ docker-compose.yml # Docker services β”œβ”€β”€ Dockerfile # Container build config β”œβ”€β”€ requirements.txt # Python dependencies └── run_server.py # Server entry point

πŸ”§ Development

Setting up Development Environment

# Clone and install git clone https://github.com/your-username/mcp-link-scan.git cd mcp-link-scan pip install -r requirements.txt # Set up environment variables cp .env.example .env # Edit .env with your settings # Start Ollama (if not using Docker) ollama serve ollama pull llama3:latest

Development Mode with Docker

# Start in development mode (hot reload enabled) docker-compose up -d # View logs docker-compose logs -f link-scan # Code changes are automatically reloaded

Running Tests

# Run all tests pytest # Run with coverage pytest --cov=src # Run specific test file pytest tests/test_link_scanner.py

Customizing Prompts

Edit src/prompts/__init__.py to customize LLM prompts:

# Video summarization prompt VIDEO_SUMMARIZE_SYSTEM = """ Your custom system prompt here... """ # Text summarization prompt TEXT_SUMMARIZE_SYSTEM = """ Your custom system prompt here... """

Configuring Whisper Model

Edit src/tools/media_handler.py:

# Change model size (tiny, base, small, medium, large) _whisper_model = whisper.load_model("base") # Default: "base"

πŸ“‹ Requirements

  • Python 3.11+

  • ffmpeg - Audio processing

  • Ollama - LLM runtime (for summarization)

  • yt-dlp - Video/audio download

  • openai-whisper - Speech-to-text

  • torch - PyTorch (for Whisper)

  • aiohttp - Async HTTP client

  • beautifulsoup4 - HTML parsing

  • fastapi - HTTP server framework

  • uvicorn - ASGI server

  • mcp - Model Context Protocol SDK

🌐 Deployment

PlayMCP Registration

  1. Deploy Server: Deploy to cloud hosting (Render, Railway, Fly.io, AWS, GCP, etc.)

  2. Get Server URL: Example: https://your-server.railway.app

  3. Register in PlayMCP: Use URL https://your-server.railway.app/messages

Important: Server URL must be publicly accessible and support HTTPS for production use.

Using with MCP Clients

Amazon Q CLI:

{ "mcpServers": { "link-scan": { "command": "python", "args": ["run_server.py"], "cwd": "/path/to/mcp-link-scan" } } }

Other MCP Clients:

{ "mcpServers": { "link-scan": { "url": "https://your-server.com/messages" } } }

🀝 Contributing

We welcome contributions! Please follow these steps:

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Make your changes

  4. Add tests for new functionality

  5. Ensure all tests pass (pytest)

  6. Commit your changes (git commit -m 'Add amazing feature')

  7. Push to the branch (git push origin feature/amazing-feature)

  8. Open a Pull Request

Development Workflow

# Install in development mode pip install -e . # Run tests pytest # Format code (if using formatters) black src/ tests/ isort src/ tests/

πŸ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

πŸ™ Acknowledgments

  • yt-dlp team for the excellent YouTube extraction library

  • OpenAI Whisper team for the speech-to-text model

  • Ollama team for the local LLM runtime

  • MCP team for the Model Context Protocol specification

  • Pydantic team for the data validation library

πŸ“ž Support

πŸ—ΊοΈ Roadmap

  • Batch processing for multiple links

  • Caching layer for improved performance

  • Export functionality (JSON, CSV, etc.)

  • Advanced analytics (sentiment analysis, topic extraction)

  • Support for more video platforms (TikTok, Vimeo, etc.)

  • WebSocket support for real-time updates

  • Integration examples with popular MCP clients

  • Custom prompt templates via API

  • Multi-language support for summaries

  • Video thumbnail extraction

πŸ“ Notes

  • Audio downloads are temporarily stored and automatically cleaned up

  • Whisper model is loaded once and reused for better performance

  • Processing time depends on video length and Whisper model size

  • YouTube videos are processed for first 7 seconds only to reduce processing time

  • All text sources (title, description, subtitles, transcription) are combined for YouTube videos

  • Summaries are limited to 3 sentences maximum

  • For production, consider using GPU for faster Whisper conversion

  • Ollama timeout is set to 5 minute for tool calls

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