video-analyzer

video-analyzer

MCP server enabling video analysis via scene detection, audio transcription, visual description, and stylistic fingerprinting, with tools for full pipeline execution and storyboard generation.

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README

Video Analyzer

Analyze videos and generate storyboard breakdowns. Extracts frames via scene detection, transcribes audio with Whisper, analyzes visuals with Claude Vision, and computes 8-field stylistic fingerprints.

Features

  • Frame Extraction — Scene-detection-based keyframe selection (or fixed intervals)
  • Audio Transcription — Timestamped transcription via OpenAI Whisper
  • Visual Analysis — Per-frame descriptions using Claude Vision
  • Stylistic Fingerprint v3 — 8-field deterministic classification (rendering class, world type, character strategy, narrative structure, visual abstraction, visual density, camera language, tonal positioning)
  • Storyboard Output — Combined shot-by-shot breakdown as .docx or .md

MCP Server

This project includes an MCP (Model Context Protocol) server so you can use video analysis directly from Claude Desktop or Claude Code.

Install via Claude Code

claude mcp add video-analyzer -s user -- uvx video-analyzer-mcp

Install via Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "video-analyzer": {
      "command": "uvx",
      "args": ["video-analyzer-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "your-key-here"
      }
    }
  }
}

MCP Tools

Tool Description
video_analyze Full pipeline: download, extract frames, transcribe, analyze, fingerprint, storyboard
video_extract_frames Extract representative frames using scene detection or fixed intervals
video_transcribe Transcribe audio with OpenAI Whisper
video_fingerprint Generate 8-field Stylistic Fingerprint v3 classification
video_check_deps Verify all required dependencies are installed

Standalone Usage

pip install -r requirements.txt
python3 analyze_video.py "https://youtube.com/watch?v=..." --output-dir ./output

Prerequisites

  • Python 3.10+
  • FFmpegbrew install ffmpeg (macOS) or apt-get install ffmpeg (Linux)
  • ANTHROPIC_API_KEY — set as an environment variable

Stylistic Fingerprint Fields

  1. Rendering Class — Stylized 3D, Flat 2D, Minimalist Line Art, Textured 2D, Mixed Media, Photoreal
  2. World Type — Stylized Real-World, Abstract Concept Space, Data/Presentation Space, Fictional Metaphor Universe
  3. Character Strategy — None, Mascot-Led, Single Narrator, Single Protagonist Arc, Ensemble Cast
  4. Narrative Structure — Direct Explanation, Step-by-Step, Problem-Solution, Analogy, Myth-Busting, etc.
  5. Visual Abstraction Index — 1 (Photorealistic) to 5 (Maximum Abstraction)
  6. Visual Density — Minimal, Sparse, Moderate, High
  7. Camera/Editing Language — Cinematic, Social Vertical Punch, Presentation Deck, Static Slides, etc.
  8. Tonal Positioning — Institutional, Corporate Professional, Gen Z Social, Child-Friendly, Dark Editorial

License

MIT

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