Transcript

glama

A Model Context Protocol (MCP) server that provides comprehensive video tools: transcript retrieval, video downloading, automatic subtitle generation, and direct audio transcription. Works with YouTube, Bilibili, Vimeo, and any platform supported by yt-dlp.

Features

• Multi-Platform Support: Works with YouTube, Bilibili, Vimeo, and any platform supported by yt-dlp
• Video Transcripts: Extract existing transcripts/captions from videos
• Video Downloads: Download videos to local storage in various formats and qualities
• Auto Subtitle Generation: Generate subtitles using OpenAI Whisper API or local Whisper
• Client Audio Transcription: audio_url fetch (allowlisted), small audio_base64, chunked uploads, optional async jobs, server-side Opus compression, structured JSON results
• Multiple URL Formats: Support for various URL formats from different platforms
• Timestamp Support: Include or exclude timestamps in transcript output
• Language Selection: Request transcripts or generate subtitles in specific languages

Tools

Tool	Description
get-transcript	Retrieve existing transcripts from video platforms
list-transcript-languages	List available transcript languages for a video
download-video	Download videos to local storage
list-downloads	List downloaded video files
generate-subtitles	Generate subtitles using AI speech-to-text
transcribe-audio	Transcribe client-provided audio (URL / base64 / path / resource URI)
transcribe_upload_start	Start chunked upload for large audio payloads
transcribe_upload_append	Append one base64 chunk to an upload session
transcribe_upload_finalize	Finish upload and run transcription
transcribe_get_job	Poll async transcription jobs
transcribe_cancel_job	Cancel an async transcription job

Prerequisites

• Node.js >= 16.0.0
• yt-dlp - Required for transcript fetching and video downloads
• ffmpeg - Required for subtitle generation, audio normalization, Opus compression, and silence-aware splitting (install a build with libopus)

Installing Dependencies

yt-dlp (required):

# Using Homebrew (macOS)
brew install yt-dlp

# Using pip
pip install yt-dlp

ffmpeg (required for subtitle generation):

# Using Homebrew (macOS)
brew install ffmpeg

# Using apt (Ubuntu/Debian)
sudo apt install ffmpeg

Local Whisper (optional, for local subtitle generation):

pip install openai-whisper

Installation

From Source

git clone <repository-url>
cd transcript-mcp
npm install
npm run build

Global Installation (after publishing)

npm install -g transcript-mcp

Configuration

For Claude Desktop / Cursor

Add the MCP server to your configuration file:

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "transcript-mcp": {
      "command": "node",
      "args": ["/path/to/transcript-mcp/dist/index.js"],
      "env": {
        "TRANSCRIPT_MCP_STORAGE_DIR": "/path/to/downloads",
        "OPENAI_API_KEY": "your-openai-api-key"
      }
    }
  }
}

Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "transcript-mcp": {
      "command": "node",
      "args": ["/path/to/transcript-mcp/dist/index.js"],
      "env": {
        "TRANSCRIPT_MCP_STORAGE_DIR": "/path/to/downloads",
        "OPENAI_API_KEY": "your-openai-api-key"
      }
    }
  }
}

Environment Variables

Variable	Description	Default
TRANSCRIPT_MCP_STORAGE_DIR	Default directory for downloaded videos	~/.transcript-mcp/downloads
OPENAI_API_KEY	OpenAI API key for Whisper-based subtitle generation	None
TRANSCRIPT_MCP_WHISPER_ENGINE	Preferred whisper engine: openai, local, or auto	auto
VIDEO_TOOLKIT_STORAGE_DIR	Legacy alias for TRANSCRIPT_MCP_STORAGE_DIR	—
VIDEO_TOOLKIT_WHISPER_ENGINE	Legacy alias for TRANSCRIPT_MCP_WHISPER_ENGINE	—
WHISPER_BINARY_PATH	Path to local whisper binary	whisper
WHISPER_MODEL_PATH	Path to whisper model (for local whisper)	Auto-download
YT_DLP_PATH	Path to yt-dlp binary	yt-dlp
FFMPEG_PATH	Path to ffmpeg binary	ffmpeg
FFPROBE_PATH	Path to ffprobe binary	Derived from FFMPEG_PATH
TRANSCRIPT_MCP_URL_ALLOWLIST	Comma-separated host patterns allowed for audio_url (e.g. *.amazonaws.com,localhost). Empty disables all audio_url fetches	empty
DEBUG	Enable debug logging	0

Usage

1. get-transcript

Retrieve existing transcripts from video platforms.

Parameters:

• url (required): Video URL
• lang (optional): Language code (e.g., 'en', 'es', 'zh')
• include_timestamps (optional): Include timestamps (default: true)

Example:

Get the transcript from https://www.youtube.com/watch?v=VIDEO_ID

2. list-transcript-languages

List available transcript languages for a video.

Parameters:

• url (required): Video URL

Example:

What transcript languages are available for https://www.youtube.com/watch?v=VIDEO_ID?

3. download-video

Download a video to local storage.

Parameters:

• url (required): Video URL to download
• output_dir (optional): Custom output directory
• filename (optional): Custom filename
• format (optional): Video format - mp4, webm, mkv (default: mp4)
• quality (optional): Quality - best, 1080p, 720p, 480p, 360p, audio (default: best)

Example:

Download this video: https://www.youtube.com/watch?v=VIDEO_ID

4. list-downloads

List all downloaded video files.

Parameters:

• directory (optional): Directory to list (default: storage directory)

Example:

List my downloaded videos

5. generate-subtitles

Generate subtitles for a local video file using AI speech-to-text.

Parameters:

• video_path (required): Absolute path to the video file
• engine (optional): openai or local (default: auto-detect)
• language (optional): Language code for transcription
• output_format (optional): srt or vtt (default: srt)

Example:

Generate subtitles for /path/to/video.mp4

6. transcribe-audio

Transcribes audio via Whisper. Prefer audio_url (server fetches bytes; configure TRANSCRIPT_MCP_URL_ALLOWLIST). Use audio_base64 only for small clips (about 60KB raw per call; larger payloads should use chunked upload or a URL). audio_path / file:// only work when the MCP host shares a filesystem with the caller (often false in sandboxed clients).

By default the server re-encodes to Opus 16 kHz mono 16 kbps before Whisper. Set skip_compression: true if you already optimized the file.

Audio longer than 5 minutes (or when async: true) returns { job_id, status: "processing" }; poll transcribe_get_job.

Parameters (one required input):

• audio_url, audio_path, audio_base64, or audio_resource_uri (file:// / data:...;base64,...)
• filename (optional): Hint when magic-byte detection is inconclusive
• skip_compression (optional): Skip Opus recompression (default: false)
• engine (optional): openai, local, or auto (default: auto)
• language (optional): Language hint for transcription
• include_timestamps (optional): When as_text is true, include [MM:SS] lines (default: true)
• as_text (optional): If true, return plain transcript text; if false, return structured JSON (default: false)
• async (optional): Force async job (default: false)

Examples:

Transcribe this presigned URL (after allowlisting the host): audio_url=...

Transcribe this audio file on the MCP host: /path/to/interview.m4a

7. transcribeupload* (chunked upload)

For large files, split the raw bytes into base64 chunks of at most max_chunk_bytes (~60KB) from transcribe_upload_start, call transcribe_upload_append for each index, then transcribe_upload_finalize. Abandoned uploads are garbage-collected after about an hour.

8. transcribe_get_job / transcribe_cancel_job

Poll or cancel async jobs created by transcribe-audio (long audio or async: true).

Subtitle Generation Engines

OpenAI Whisper API

• Pros: High accuracy, no local setup needed, supports 50+ languages
• Cons: Requires API key, costs per audio minute
• Setup: Set OPENAI_API_KEY environment variable

Local Whisper

• Pros: Free, runs locally, no API limits
• Cons: Requires setup, uses local CPU/GPU
• Setup: pip install openai-whisper

The tool auto-detects which engine to use:

1. If OPENAI_API_KEY is set, uses OpenAI Whisper
2. If local whisper is installed, uses local whisper
3. Returns an error if neither is available

For transcribe-audio, auto uses OpenAI first and falls back to local whisper when local whisper is available.

Example Workflows

Download and Generate Subtitles

1. Download this video: https://www.youtube.com/watch?v=VIDEO_ID
2. Generate subtitles for the downloaded file

Summarize a Video

Get the transcript from https://www.youtube.com/watch?v=VIDEO_ID and summarize the key points

Create Captions for Videos Without Subtitles

1. Download the video: https://vimeo.com/123456789
2. Generate English subtitles for it

Supported Platforms

Any platform supported by yt-dlp, including:

• YouTube
• Bilibili
• Vimeo
• Twitter/X
• TikTok
• Twitch
• And many more...

Full list: https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md

Project Structure

transcript-mcp/
├── src/
│   ├── index.ts              # Main MCP server entry point
│   ├── transcript-fetcher.ts # Transcript fetching using yt-dlp
│   ├── video-downloader.ts   # Video download functionality
│   ├── subtitle-generator.ts # AI-powered subtitle generation
│   ├── config.ts             # Configuration management
│   ├── url-detector.ts       # Platform detection from URLs
│   ├── parser.ts             # Transcript parsing (SRT, VTT, JSON)
│   └── errors.ts             # Custom error classes
├── test/
│   └── transcript.test.ts    # Unit tests
├── dist/                     # Compiled JavaScript (after build)
└── package.json

Development

# Build
npm run build

# Test
npm test

# Development mode
npm run dev

Troubleshooting

"yt-dlp is not installed"

brew install yt-dlp
# or
pip install yt-dlp

"ffmpeg is not installed"

brew install ffmpeg

"ffprobe is not installed"

brew install ffmpeg

"No Whisper engine available"

Either:

• Set OPENAI_API_KEY environment variable, or
• Install local whisper: pip install openai-whisper

Download issues

• Check if the video is publicly accessible
• Some platforms may have rate limits
• Private/restricted videos cannot be downloaded

Subtitle generation is slow

• OpenAI Whisper API is faster than local
• Local whisper performance depends on your hardware
• Consider using a smaller model for local whisper

License

MIT

Acknowledgments

• yt-dlp for video platform support
• OpenAI Whisper for speech-to-text
• Model Context Protocol for the MCP framework