by sealingp
Transcribe for YouTube and other platforms. Extract accurate transcript of a YouTube video for accessibility, analysis,
Downloads videos/audio from major platforms (YouTube, TikTok, Instagram, etc.) and converts speech to text using OpenAI's Whisper model. Supports multiple languages and audio formats.
You can install Video & Audio Text Extraction in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Video & Audio Text Extraction is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Strong directory entry: Video & Audio Text Extraction surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Video & Audio Text Extraction for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We wired Video & Audio Text Extraction into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, Video & Audio Text Extraction benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: Video & Audio Text Extraction is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated Video & Audio Text Extraction against two servers with overlapping tools; this profile had the clearer scope statement.
Video & Audio Text Extraction is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Video & Audio Text Extraction is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Video & Audio Text Extraction has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Video & Audio Text Extraction reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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An MCP server that provides text extraction capabilities from various video platforms and audio files. This server implements the Model Context Protocol (MCP) to provide standardized access to audio transcription services.
This service supports downloading videos and extracting audio from various platforms, including but not limited to:
For a complete list of supported platforms, please visit yt-dlp supported sites.
This project utilizes OpenAI's Whisper model for audio-to-text processing through MCP tools. The server exposes four main tools:
This server is built using the Model Context Protocol, which provides:
Important: On first run, the system will automatically download the Whisper model file (approximately 1GB). This process may take several minutes to tens of minutes, depending on your network conditions. The model file will be cached locally and won't need to be downloaded again for subsequent runs.
When using uv no specific installation is needed. We will use uvx to directly run the video extraction server:
curl -LsSf https://astral.sh/uv/install.sh | sh
FFmpeg is required for audio processing. You can install it through various methods:
# Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg
# Arch Linux
sudo pacman -S ffmpeg
# MacOS
brew install ffmpeg
# Windows (using Chocolatey)
choco install ffmpeg
# Windows (using Scoop)
scoop install ffmpeg
Add to your Claude/Cursor settings:
"mcpServers": {
"video-extraction": {
"command": "uvx",
"args": ["mcp-video-extraction"]
}
}
The service can be configured through environment variables:
WHISPER_MODEL: Whisper model size (tiny/base/small/medium/large), default: 'base'WHISPER_LANGUAGE: Language setting for transcription, default: 'auto'YOUTUBE_FORMAT: Video format for download, default: 'bestaudio'AUDIO_FORMAT: Audio format for extraction, default: 'mp3'AUDIO_QUALITY: Audio quality setting, default: '192'TEMP_DIR: Temporary file storage location, default: '/tmp/mcp-video'DOWNLOAD_RETRIES: Number of download retries, default: 10FRAGMENT_RETRIES: Number of fragment download retries, default: 10SOCKET_TIMEOUT: Socket timeout in seconds, default: 30GPU Acceleration:
Model Size Adjustment:
Use SSD storage for temporary files to improve I/O performance
This server can be used with any MCP-compatible client, such as:
For more information about MCP, visit Model Context Protocol.
For Chinese version of this documentation, please refer to README_zh.md
MIT
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.