YouTube智能分析工具

Migration notice: Active development has moved to fabioc-aloha/youtube-mcp-tools. This repository remains available as the historical standalone-server source while the canonical project assumes release ownership.

Think • Build • Deploy — Transform YouTube into a knowledge engine for AI agents

An intelligent Model Context Protocol (MCP) server that turns AI assistants into YouTube research powerhouses. Go beyond simple video searches—synthesize knowledge across multiple videos, extract structured concepts, and conduct deep research with AI-native tools.

✨ What Makes This Different

vs Other YouTube MCP Servers

The first YouTube MCP with an intelligence layer — we don't just fetch data, we understand it.

vs Raw YouTube API

🚀 Quick Start

Installation

# Install globally
npm install -g youtube-mcp-tools

# Or use directly with npx (no install needed)
npx youtube-mcp-tools

VS Code / GitHub Copilot Setup

Add to your VS Code settings.json:

{
  "mcp": {
    "servers": {
      "youtube": {
        "command": "npx",
        "args": ["-y", "youtube-mcp-tools"],
        "env": {
          "YOUTUBE_API_KEY": "your_api_key"
        }
      }
    }
  }
}

Claude Desktop Setup

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "youtube": {
      "command": "npx",
      "args": ["-y", "youtube-mcp-tools"],
      "env": {
        "YOUTUBE_API_KEY": "your_api_key"
      }
    }
  }
}

🧠 Intelligence Layer

The magic happens in the intelligence layer—purpose-built for AI agents:

🔬 Research Synthesis

"Compare how these 5 videos explain React Server Components" → Returns consensus points, controversies, unique insights, and citations

📚 Concept Extraction

"What concepts does this tutorial cover?" → Returns structured topics with difficulty levels, prerequisites, and relationships

✂️ Smart Chunking

"Prepare this 2-hour lecture for analysis" → Returns AI-optimized chunks respecting sentence boundaries and context

🛠️ Tools Overview

40 powerful tools + 3 MCP resources organized by capability:

📖 Full Tool Reference → | 📚 Examples → | 🎬 Demo Guide →

🛡️ Production Ready

Built for reliability in production environments:

🎯 Use Cases

📚 Research & Learning

• Synthesize knowledge from multiple educational videos
• Generate flashcards and quizzes from any tutorial
• Extract structured learning paths with prerequisites
• Compare teaching approaches across instructors

📊 Content Analysis

• Analyze competitor content strategies
• Track trending topics in your niche
• Identify content gaps and opportunities
• Benchmark channel performance

🎨 Content Creation

• Repurpose videos into blog posts, tweets, newsletters
• Generate podcast show notes from interviews
• Create short-form scripts from long content
• Plan content strategy with gap analysis

🤖 AI Agent Workflows

• Build YouTube-powered research assistants
• Create automated content summarization pipelines
• Develop knowledge base builders from video content

🏗️ Architecture

┌───────────────────────────────────────────────┐
│                 MCP Protocol                  │
├───────────────────────────────────────────────┤
│  Tools (40)  │  Prompts (5)  │  Resources (3) │
├──────────────┴───────────────┴────────────────┤
│              Intelligence Layer               │
│  ┌─────────┐ ┌──────────┐ ┌───────────┐       │
│  │ Chunker │ │ Analyzer │ │ Synthesis │       │
│  ├─────────┤ ├──────────┤ ├───────────┤       │
│  │  Quiz   │ │ Learning │ │ Repurpose │       │
│  │Generator│ │   Path   │ │  Content  │       │
│  ├─────────┤ ├──────────┤ ├───────────┤       │
│  │ Watch   │ │ Content  │ │Flashcards │       │
│  │ History │ │   Gaps   │ │           │       │
│  └─────────┘ └──────────┘ └───────────┘       │
├───────────────────────────────────────────────┤
│           Semantic Search Layer               │
│  ┌─────────┐ ┌──────────┐ ┌───────────┐       │
│  │Embedding│ │  Vector  │ │ Clustering│       │
│  │ Service │ │  Store   │ │  (k-means)│       │
│  └─────────┘ └──────────┘ └───────────┘       │
├───────────────────────────────────────────────┤
│         Production Infrastructure             │
│  ┌─────────┐ ┌──────────┐ ┌───────────┐       │
│  │  Rate   │ │ Graceful │ │   Input   │       │
│  │ Limiter │ │ Shutdown │ │ Validator │       │
│  └─────────┘ └──────────┘ └───────────┘       │
├───────────────────────────────────────────────┤
│        Cache Layer (LRU + Vectors)            │
├───────────────────────────────────────────────┤
│         YouTube Data API v3 + Transcripts     │
└───────────────────────────────────────────────┘

📈 Roadmap

• [X] Phase 1: Core YouTube API integration (18 tools)
• [X] Phase 2: Intelligence layer (analysis, synthesis)
• [X] Phase 3: Learning tools (flashcards, quizzes, learning paths)
• [X] Phase 4: Creator tools (content gaps, repurposing)
• [X] Phase 5: Personal insights (watch history analysis)
• [X] Phase 6: Semantic search & embeddings
• [ ] Phase 7: Knowledge graph & entity extraction
• [ ] Phase 8: Real-time streaming analysis

📋 Full Project Plan → | 🔮 Vision →

🔧 Development

# Development mode with hot reload
npm run dev

# Run tests
npm test

# Build for production
npm run build

📊 API Quota Guide

YouTube Data API quota: 10,000 units/day

💡 Pro Tip: Transcripts are quota-free. Use youtube_transcript and youtube_transcript_search for deep content analysis without burning quota.

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

📄 License

MIT © 2026 Fabio Caversan

Built with 🧠 by Alex • Transforming how AI agents understand video content

_{© 2026 Fabio Caversan • Think. Build. Deploy.}