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.

Features

4 specialized tools for different comment analysis needs
No authentication required - uses web scraping
Context-efficient statistics tool to avoid token bloat
Built-in capacity planning with memory and timeout limits
Engagement analysis with actual like-count sorting

MCP Client Configuration

Add this configuration block to your MCP client (e.g., Claude Desktop):

"ytcomment-mcp": {
  "command": "uv",
  "args": [
    "run",
    "--directory",
    "/Users/chad.kunsman/Documents/PythonProject/ytcomment_mcp",
    "src/server.py"
  ]
}

Available Tools

1. `download_youtube_comments`

Download raw comment data with full details.

Parameters: video_id, limit (1-10000), sort (0=popular, 1=recent)
Returns: Full comment dataset with all metadata
Use case: When you need complete comment data for analysis

2. `get_comment_stats`

Get statistical analysis without full comment data (context-efficient).

Parameters: video_id, limit, sort
Returns: Statistics + 5 sample comments (~200 tokens vs ~25,000)
Use case: Quick engagement insights without context bloat
Triggers: "how engaged", "what's the engagement", "comment patterns"

3. `search_comments`

Search for specific terms within comments.

Parameters: video_id, search_term, limit, sort
Returns: Matching comments + search metadata
Use case: Finding mentions, sentiment analysis, topic research
Triggers: "find comments about", "search for", "mentions of"

4. `get_top_comments_by_likes`

Get most-liked comments sorted by actual like count (not YouTube's "popular").

Parameters: video_id, top_count (1-100), sample_size (100-2000, default: 500)
Returns: Top comments ranked by likes + engagement stats
Use case: Finding viral comments that YouTube's algorithm might not surface first
Triggers: "most popular", "most liked", "viral comments", "best comments"

Quick Start

# Install dependencies
uv venv && source .venv/bin/activate
uv pip install -e .

# Test functionality
python test_server.py

# Run MCP server
python src/server.py

Data Structure

Each comment contains 11 fields:

cid, text, time, time_parsed, author, channel
votes (likes), replies, photo, heart, reply

Capacity: ~1.8KB memory, ~25 tokens per comment

Key Limitations & Performance

Flat structure: No hierarchical reply threading
Mixed results: Top-level + replies mixed together (~10%/90% split)
Rate limited: Built-in delays, ~30-90 sec per 500-1,000 comments
Timeout handling: Larger requests may timeout; tool includes fallbacks
No API quotas: Web scraping approach, but respect YouTube's terms

Performance Optimizations

Reduced timeouts: 90s default (was 120s) for faster failure detection
Smaller defaults: 500 comment samples (was 1000) for better reliability
Timeout fallbacks: get_top_comments_by_likes tries recent sort if popular fails
Context efficiency: Stats tool uses ~200 tokens vs ~25,000 for full data

Example Usage

# Get engagement overview (context-efficient)
stats = await get_comment_stats("dQw4w9WgXcQ", limit=1000)

# Find specific mentions
results = await search_comments("dQw4w9WgXcQ", "rickroll", limit=500) 

# Get viral comments by actual likes
top = await get_top_comments_by_likes("dQw4w9WgXcQ", top_count=20)

Built with FastMCP and youtube-comment-downloader.

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables AI systems to download and analyze YouTube video comments through 4 specialized tools without requiring API keys, supporting engagement analysis, comment search, and statistics gathering.

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