brew install nhatvu148/tap/video-transcriber-mcp

cargo install video-transcriber-mcp

# macOS (Intel)
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-x86_64-apple-darwin.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/

# macOS (Apple Silicon)
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-aarch64-apple-darwin.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/

# Linux (x86_64) — no ARM64 Linux build, see issue #13; use `cargo install`
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-x86_64-unknown-linux-gnu.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/

# Windows: Download .zip from releases page

/plugin marketplace add nhatvu148/video-transcriber-mcp-rs
/plugin install video-transcriber@nhatvu148-tools

# 1. Install Task (if not already installed)
brew install go-task/tap/go-task

# 2. Complete setup (build + download model)
task setup

# 3. Run a quick test
task test:quick

# Done! 🎉

task setup           # Complete project setup
task test:quick      # Test with short video
task benchmark       # Run performance benchmark
task deps:check      # Check dependencies
task download:base   # Download base model
task help            # Show all commands

video-transcriber-mcp
# or explicitly:
video-transcriber-mcp --transport stdio

# Start HTTP server on default port (8080)
video-transcriber-mcp --transport http

# Custom host and port
video-transcriber-mcp --transport http --host 0.0.0.0 --port 3000

{
  "mcpServers": {
    "video-transcriber-mcp": {
      "url": "http://localhost:8080/mcp"
    }
  }
}

video-transcriber-mcp --help

Options:
  -t, --transport <TRANSPORT>  Transport mode [default: stdio] [possible values: stdio, http]
      --host <HOST>            Host address for HTTP transport [default: 127.0.0.1]
  -p, --port <PORT>            Port for HTTP transport [default: 8080]
  -h, --help                   Print help
  -V, --version                Print version

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# macOS
brew install yt-dlp

# Linux
pip install yt-dlp

# Windows
winget install yt-dlp.yt-dlp

# macOS
brew install ffmpeg

# Linux
sudo apt install ffmpeg  # Debian/Ubuntu
sudo dnf install ffmpeg  # Fedora

# Windows
choco install ffmpeg

# Clone the repository
git clone https://github.com/nhatvu148/video-transcriber-mcp-rs.git
cd video-transcriber-mcp-rs

# Build the project
cargo build --release

# The binary will be at: target/release/video-transcriber-mcp-rs

# Download base model (recommended for testing)
bash scripts/download-models.sh base

# Or download all models
bash scripts/download-models.sh all

{
  "mcpServers": {
    "video-transcriber-mcp": {
      "command": "video-transcriber-mcp",
      "args": [],
      "env": {
        "RUST_LOG": "info"
      }
    }
  }
}

{
  "mcpServers": {
    "video-transcriber-mcp": {
      "command": "/absolute/path/to/video-transcriber-mcp-rs/target/release/video-transcriber-mcp",
      "args": [],
      "env": {
        "RUST_LOG": "info"
      }
    }
  }
}

Please transcribe this YouTube video: https://www.youtube.com/watch?v=VIDEO_ID

Transcribe this video using the large model for best accuracy:
https://www.youtube.com/watch?v=VIDEO_ID

Transcribe this local video file: /Users/myname/Videos/meeting.mp4

Transcribe this Spanish video: https://www.youtube.com/watch?v=VIDEO_ID
(language: es, model: medium)

video-id-title.txt   # Plain text transcript
video-id-title.json  # JSON with metadata and timestamps
video-id-title.md    # Markdown with video info

# How to Build Fast Software

**Video:** https://www.youtube.com/watch?v=example
**Platform:** YouTube
**Channel:** Tech Channel
**Duration:** 600s

---

## Transcript

The key to building fast software is understanding...

---

*Transcribed using whisper.cpp (Rust) - Model: base*

# Comma-separated. Added on top of the loopback defaults, so local
# development and health checks keep working.
export MCP_ALLOWED_HOSTS=mcp.example.com,mcp.example.com:8080

# On Fly:
fly secrets set MCP_ALLOWED_HOSTS=your-app.fly.dev

# Option 1 (preferred on headless / Linux): a Netscape-format cookies file.
# Export it however you like — e.g. a QR-login flow — then point at it.
export YT_DLP_COOKIES=/path/to/cookies.txt

# Option 2: read cookies straight from a logged-in local browser.
# One of: chrome, brave, edge, firefox, safari, chromium, opera, vivaldi.
# Ignored when YT_DLP_COOKIES is set.
export YT_DLP_COOKIES_FROM_BROWSER=chrome

# POST audio to a remote HTTP worker (e.g. a serverless GPU) instead of
# running whisper-rs locally. Endpoint must accept multipart {audio, model,
# language} and return JSON {transcript, segments[], language, duration_s}.
export REMOTE_WHISPER_URL=https://your-worker.example.com/transcribe

# Debug build
cargo build

# Release build (optimized)
cargo build --release

# Run tests
cargo test

# Run with logging
RUST_LOG=debug cargo run -- --url "https://youtube.com/watch?v=example"

src/
├── main.rs           # CLI + transport selection (stdio / streamable HTTP)
├── lib.rs            # public API for embedders
├── mcp/              # MCP server: tool definitions and handlers
├── transcriber/      # the pipeline: yt-dlp → ffmpeg → whisper.cpp
├── embeddings.rs     # passage embeddings, used by `search_transcripts`
└── utils/            # paths