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YouTube Comment Downloader MCP Server

@suckerfish

基本情報

カテゴリ

メディアとデザイン

トランスポート

stdio

作者

suckerfish

YouTube Comment Downloader MCP Server について

YouTube Comment Downloader MCP server that allows AI systems to download and analyze YouTube video comments without requiring API keys

#ytcomment#youtube#comment-downloader

YouTube Comment Downloader MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "ytcomment_mcp": {
      "command": "uv",
      "args": [
        "venv",
        "&&",
        "source",
        ".venv/bin/activate"
      ]
    }
  }
}

YouTube Comment Downloader MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is YouTube Comment Downloader MCP Server?

A Model Context Protocol (MCP) server that provides AI systems with the ability to download and analyze YouTube video comments without requiring API keys. It uses web scraping, so no authentication is needed.

How to use YouTube Comment Downloader MCP Server?

Install dependencies with uv venv && source .venv/bin/activate && uv pip install -e ., test with python test_server.py, and run the server with python src/server.py. For MCP clients (e.g., Claude Desktop), add a configuration block using uv as the command pointing to src/server.py. The server exposes four tools: download_youtube_comments, get_comment_stats, search_comments, and get_top_comments_by_likes.

Key features of YouTube Comment Downloader MCP Server

  • Four specialized tools for different comment analysis needs
  • No authentication required – uses web scraping
  • Context-efficient statistics tool (~200 tokens vs ~25,000 for full data)
  • Built-in capacity planning with memory and timeout limits
  • Engagement analysis with actual like-count sorting

Use cases of YouTube Comment Downloader MCP Server

  • Download complete comment datasets for analysis with full metadata
  • Get quick engagement insights without consuming large context windows
  • Search for specific mentions, perform sentiment analysis, or research topics
  • Find viral comments by actual likes that YouTube's algorithm may not surface first

よくある質問

1

What are the key limitations?

The data has a flat structure (no hierarchical reply threading), top-level and reply comments are mixed (~10%/90% split), scraping is rate limited (~30–90 seconds per 500–1,000 comments), and larger requests may time out. There are no API quotas, but YouTube's terms must be respected.

2

What parameters do the tools accept?

Common parameters: `video_id`, `limit` (1–10,000), and `sort` (0=popular, 1=recent). `search_comments` adds a `search_term` parameter. `get_top_comments_by_likes` accepts `top_count` (1–100) and `sample_size` (100–2,000, default 500).

3

How is context usage optimized?

The `get_comment_stats` tool returns only statistics and five sample comments, using approximately 200 tokens compared to roughly 25,000 tokens for the full comment data.

4

Is authentication or an API key required?

No. The server uses web scraping to retrieve comments, so no YouTube API key or authentication is needed.

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