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video-analyzer

video-analyzer

Local
github.com/BrightWayAIMedia & Design0Updated 33w ago
Repository

About video-analyzer

video-analyzer analyzes videos and creates shot-by-shot storyboard breakdowns for video reviewers and creators. It combines scene-based frame extraction, Whisper transcription, Claude Vision descriptions, and an eight-field stylistic fingerprint.

Key features

  1. 01Scene-based or interval frame extraction
  2. 02Timestamped Whisper transcription
  3. 03Per-frame Claude Vision analysis
  4. 04Eight-field stylistic classification
  5. 05Storyboard export as DOCX or Markdown

Use cases

  1. 01Create a shot-by-shot video breakdown
  2. 02Transcribe and inspect video content
  3. 03Classify a video's visual style
  4. 04Export a storyboard for review

MCP support

The MCP server makes video analysis available to clients such as Claude Desktop and Claude Code. Its tools cover the full analysis pipeline, frame extraction, transcription, fingerprinting, and dependency checks.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "brightwayai-video-analyzer": {
      "command": "uvx",
      "args": [
        "video-analyzer-mcp"
      ]
    }
  }
}
Run commanduvx video-analyzer-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypivideo-analyzer-mcp0.1.2

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project 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
ToolDescription
video_analyzeFull pipeline: download, extract frames, transcribe, analyze, fingerprint, storyboard
video_extract_framesExtract representative frames using scene detection or fixed intervals
video_transcribeTranscribe audio with OpenAI Whisper
video_fingerprintGenerate 8-field Stylistic Fingerprint v3 classification
video_check_depsVerify 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+
  • FFmpeg — brew 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

Comments

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