JangHyuckYun

JangHyuckYun / mcp-youtube-intelligence

Public

MCP server for YouTube transcript extraction, channel monitoring, and content intelligence

40
0
100% credibility
Found Feb 19, 2026 at 31 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A server that processes YouTube videos to provide efficient summaries, topic breakdowns, entity mentions, comment analysis, and channel monitoring for integration with AI assistants.

How It Works

1
📺 Find the YouTube smart analyzer

You hear about a handy tool that turns long YouTube videos into quick, useful summaries without wasting time.

2
🛠️ Set it up on your computer

With one easy download, everything is ready to use right away, no complicated steps needed.

3
✨ Paste a video link for magic

Just share a YouTube video URL and instantly get a short summary, key topics, and important names mentioned.

4
🤖 Link to your AI chat buddy

Connect it to your favorite AI helper so you can ask questions about videos naturally in chat.

5
🔍 Explore comments and channels

See what viewers think with mood checks on comments or watch new videos from favorite channels automatically.

🎉 Insights in minutes, not hours

Now you quickly understand videos, spot trends, and learn faster, saving tons of watching time every day.

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AI-Generated Review

What is mcp-youtube-intelligence?

This Python MCP server pulls YouTube transcripts, channel feeds, and video metadata, then crunches them server-side into compact summaries (~300 tokens), topic segments, entities, and comment sentiment—slashing the 5k-50k token dumps other tools force into your LLM context. Run it as an mcp github server via CLI (`mcp-yt transcript URL`) or hook it into clients like Claude Desktop or Cursor for seamless video intel. Pairs with mcp server ai workflows, storing results in SQLite or Postgres for fast reuse.

Why is it gaining traction?

It beats basic mcp youtube scrapers by handling heavy lifting (summarization, entity extraction, RSS channel monitoring) before hitting your LLM, with optional local models like Ollama for zero-cost runs. Devs dig the mcp github copilot vscode/intellij integration, batch CLI for playlists, and transcript search—check mcp server examples or github registry for quick mcp server python setups. Token savings alone make it a no-brainer for content-heavy agents.

Who should use this?

AI agent builders chaining YouTube analysis into n8n or custom bots, market researchers tracking competitor channels/content sentiment, or devs prototyping mcp github project manager tools that auto-summarize lectures/playlists. Ideal for anyone tired of manual video notes or bloated prompts in mcp server tutorial flows.

Verdict

Grab it if you're in the mcp server list experimenting with Python YouTube tools—solid docs, CLI, and tests make the 17 stars and 1.0% credibility score forgivable for alpha stage. Watch mcp github issues for polish, but it's production-ready for low-stakes channel/content intel today.

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