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haithamelmengad

popcorn

by haithamelmengad
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Quick Start

# Install FFmpeg (required)
brew install ffmpeg                    # macOS
sudo apt install ffmpeg                # Ubuntu/Debian

# Install Popcorn
git clone https://github.com/anthropics/popcorn.git
cd popcorn && npm install && npm run build

# Optional: Install a transcription backend
pip install mlx-whisper                # Apple Silicon (fastest)
pip install openai-whisper             # Any platform

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

{
  "mcpServers": {
    "popcorn": {
      "command": "node",
      "args": ["/path/to/popcorn/dist/index.js"]
    }
  }
}

Key Features:

  • Scene Detection — Captures frames at visual transitions, not fixed intervals

  • Local Transcription — 4 backend options (mlx-whisper, faster-whisper, whisper-cpp, whisper)

  • Inline Images — Returns key frames directly in MCP responses

  • Smart Presets — Auto-configures for screencasts, presentations, movies, interviews

  • Zero Config — Just pass a video path and it works

  • Privacy First — Everything runs locally, no data leaves your machine

Related MCP server: yt

Documentation

Getting Started

Guides

Reference

How It Works

Core Components:

  1. FFprobe — Extracts video metadata (duration, resolution, codecs)

  2. FFmpeg Scene Detection — Finds visual transitions using select='gt(scene,N)' filter

  3. Parallel Frame Extraction — Captures JPEGs at scene change timestamps

  4. Multi-Backend Transcription — Whisper variants convert audio to timestamped text

  5. Analysis Bundle — Results saved to .popcorn/ directory

  6. MCP Response — Returns metadata + inline base64 images

Video File ──▶ FFprobe ──▶ FFmpeg ──▶ Whisper ──▶ Analysis Bundle
                 │           │          │              │
                 ▼           ▼          ▼              ▼
              metadata    frames    transcript    MCP Response

MCP Tools

Tool

Description

popcorn_analyze

Main analysis — extracts frames, transcribes audio, returns results

popcorn_suggest

Probe video metadata and get recommended settings

popcorn_presets

List available video types and objectives

popcorn_backends

Detect your system and show transcription options

popcorn_read

Read transcript slices with time filtering

Basic Usage

{
  "tool": "popcorn_analyze",
  "arguments": {
    "path": "/path/to/video.mp4"
  }
}

With Presets

{
  "tool": "popcorn_analyze",
  "arguments": {
    "path": "/path/to/video.mp4",
    "videoType": "screencast",
    "objective": "detailed"
  }
}

Video Types

Type

Best For

Scene Detection

screencast

Tutorials, coding sessions, UI demos

Low threshold

presentation

Slides, lectures, keynotes

Slide transitions

movie

Films, TV shows

Balanced

interview

Podcasts, talking heads

Transcription priority

surveillance

Security footage, dashcam

High threshold

sports

Live events, fast action

High frame rate

Objectives

Objective

Use When

summary

Quick overview needed

detailed

Don't miss anything

find_moment

Searching for specific content

transcribe

Audio/speech is most important

visual_only

Only care about visuals

quick_scan

Fast preview needed

Transcription Backends

Popcorn auto-detects your system and recommends the best backend.

Backend Comparison

Backend

Speed

Best For

Install

mlx-whisper

Fastest

Apple Silicon (M1/M2/M3/M4)

pip install mlx-whisper

faster-whisper

Fast

NVIDIA GPUs

pip install faster-whisper

whisper-cpp

Moderate

Cross-platform

brew install whisper-cpp

whisper

Slow

Most compatible

pip install openai-whisper

Processing Times (60-min video)

Backend

Time

mlx-whisper

3-8 min

faster-whisper

5-10 min

whisper-cpp

10-20 min

whisper

30-60 min

Force a Backend

{
  "tool": "popcorn_analyze",
  "arguments": {
    "path": "/path/to/video.mp4",
    "backend": "mlx-whisper"
  }
}

Configuration

All Parameters

Parameter

Type

Description

path

string

Required. Absolute path to video file

videoType

string

Video type preset

objective

string

Analysis objective preset

transcribe

boolean

Enable/disable transcription

backend

string

Transcription backend

model

string

Whisper model (tiny, base, small, medium, large)

language

string

Language code (e.g., en, es, fr)

frameMode

string

scene or interval

sceneThreshold

number

Scene sensitivity (0-1)

maxFrames

number

Maximum frames to extract

inlineFrames

number

Frames to return as base64

Output Structure

.popcorn/<video>_<timestamp>/
├── analysis.json          # Full metadata
├── transcript.txt         # Plain text
├── transcript.json        # Timestamped segments
├── transcript.chunks.json # LLM-friendly chunks
└── assets/
    ├── audio.wav
    └── frames/
        ├── scene_000001.jpg
        └── ...

Troubleshooting

FFmpeg not found

brew install ffmpeg          # macOS
sudo apt install ffmpeg      # Ubuntu/Debian

No transcription backend

pip install mlx-whisper      # Apple Silicon
pip install openai-whisper   # Any platform

Too few frames detected

{ "sceneThreshold": 0.15, "minSceneInterval": 2 }

Too many frames detected

{ "sceneThreshold": 0.5, "minSceneInterval": 10 }

See Troubleshooting Guide for more solutions.

Development

npm install          # Install dependencies
npm run build        # Build
npm run dev          # Development mode
npm start            # Run server

Project Structure

popcorn/
├── src/
│   ├── index.ts        # MCP server
│   ├── analyze.ts      # Analysis pipeline
│   ├── ffmpeg.ts       # Video processing
│   ├── transcribe.ts   # Multi-backend transcription
│   ├── presets.ts      # Video type presets
│   └── commands.ts     # Shell execution
├── docs/               # Documentation
└── skills/             # Agent skills

Contributing

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing)

  3. Commit changes (git commit -m 'Add amazing feature')

  4. Push to branch (git push origin feature/amazing)

  5. Open a Pull Request

License

MIT License — see LICENSE for details.

Acknowledgments

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