yt-analytics-mcp

MCP server for YouTube channel deep analytics. Extracts transcripts, computes quantitative metrics (WPM, profanity, humor taxonomy, rhetoric fingerprint, title formula), and generates multi-creator comparison dashboards.

Tools

Related MCP server: YouTube MCP

Quick Start

# Install
cd yt-analytics-mcp
npm install

# Add to Claude Code settings
# ~/.claude/settings.json → mcpServers

Claude Code Config

{
  "mcpServers": {
    "yt-analytics": {
      "command": "node",
      "args": ["F:/tools/yt-analytics-mcp/src/index.js"],
      "env": {
        "YT_ANALYTICS_DATA": "F:/tools/yt-analytics-mcp/data"
      }
    }
  }
}

Usage

Analyze a single channel

Analyze @BennJordan's channel (20 videos, tech niche)
→ calls yt_analyze_channel with channel="@BennJordan", limit=20, niche="tech"

Returns structured JSON with per-video and aggregate metrics:

• WPM (words per minute) with niche baseline comparison

• Profanity density per minute

• Humor taxonomy (crude, self-deprecating, confrontational, hyperbolic, absurd)

• Rhetoric fingerprint (connector word frequencies: but, so, because, if you, etc.)

• Title formula (CAPS usage, exclamation marks, parentheticals)

Build a comparison dashboard

Compare all analyzed channels in a tabbed dashboard
→ calls yt_compare_creators

Generates a self-contained HTML file with:

• Sticky tab bar for switching between creators

• KPI cards (videos, words, runtime, median WPM, profanity, avg length)

• WPM horizontal bar chart with niche average line

• Video structure timeline

• Humor taxonomy doughnut chart

• Profanity density bar chart

• Rhetoric radar chart

• Video length vs. speech rate bubble chart

• Title formula badges

• Opening line pattern breakdown

• Connector language grid

• Per-video data table

• Written analysis

Workflow

1. yt_analyze_channel → analyze @Creator1 (cached)
2. yt_analyze_channel → analyze @Creator2 (cached)
3. yt_analyze_channel → analyze @Creator3 (cached)
4. yt_compare_creators → build tabbed dashboard from all cached analyses

The skill layer (/yt-analytics) adds Gemini qualitative analysis on top — editing mechanics, humor quotes, video structure timelines, energy modulation — then merges with the MCP's quantitative data.

Niche Baselines

Requirements

• Node.js 18+

• Python 3.10+ with yt-dlp and youtube-transcript-api installed

• No API keys needed

Data Storage

Analyses are cached as JSON files in the data/ directory (configurable via YT_ANALYTICS_DATA env var). Transcript files are stored in data/transcripts/.

Architecture

yt-analytics-mcp/
├── src/
│   └── index.js          # MCP server + HTML generator
├── scripts/
│   └── analyze_channel.py # Transcript extraction + quantitative analysis
├── data/                  # Cached analyses (JSON) + transcripts
├── templates/             # Custom HTML templates (optional)
├── package.json
└── README.md

The MCP server wraps the Python analysis script and adds:

• Result caching and management

• Multi-creator comparison HTML generation

• Structured tool interfaces for Claude Code

The companion /yt-analytics skill orchestrates the full pipeline: MCP quantitative analysis → Gemini qualitative pass → merged report.