YouTube Comment Downloader MCP Server
@suckerfish
About 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
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"ytcomment_mcp": {
"command": "uv",
"args": [
"venv",
"&&",
"source",
".venv/bin/activate"
]
}
}
}Tools
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Overview
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
FAQ from YouTube Comment Downloader MCP Server
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.
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).
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.
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.
Frequently asked questions
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.
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).
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.
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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