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video-analyzer-mcp

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video-analyzer-mcp

MCP server for video analysis - extracts frames, transcribes audio, analyzes visuals with Claude, and generates storyboard breakdowns

pipPyPI
Version
0.1.2
Weekly downloads
101
Maintainers
1

Video Analyzer MCP Server

An MCP (Model Context Protocol) server that analyzes videos and generates storyboard breakdowns. Extract frames, transcribe audio, analyze visuals with Claude Vision, and compute stylistic fingerprints — all usable directly from Claude Desktop or Claude Code.

Tools

ToolDescription
video_analyzeFull pipeline: download, extract frames, transcribe, visual analysis, stylistic fingerprint, storyboard document
video_extract_framesExtract representative frames using scene detection or fixed intervals
video_transcribeTranscribe audio with OpenAI Whisper (timestamped, speaker-labeled)
video_fingerprintGenerate an 8-field Stylistic Fingerprint v3 classification
video_check_depsVerify all required dependencies are installed

Prerequisites

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

Installation

From PyPI

pip install video-analyzer-mcp

For Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

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

For Claude Code

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

Usage Examples

Once installed, you can ask Claude:

  • "Analyze this video and create a storyboard" — runs video_analyze
  • "Extract frames from this YouTube video" — runs video_extract_frames
  • "Transcribe the audio from this video" — runs video_transcribe
  • "What's the stylistic fingerprint of this video?" — runs video_fingerprint
  • "Check if video analyzer dependencies are installed" — runs video_check_deps

Stylistic Fingerprint Fields

The fingerprint classifier produces 8 deterministic fields:

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

License

MIT

Keywords

analysis

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