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YouTube Comment Downloader MCP Server

@suckerfish

代码仓库

基本信息

分类

媒体与设计

传输方式

stdio

作者

suckerfish

关于 YouTube Comment Downloader MCP Server

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

YouTube Comment Downloader MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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