The YouTube Translate MCP is a specialized tool that allows AI assistants to "read" and understand YouTube videos by accessing their dialogue. It acts as a bridge between video content and large language models, making it possible to extract information from a video without needing to watch it. This makes it an essential utility for anyone who wants to quickly grasp the main points of a tutorial, lecture, or presentation through a simple text-based interface. Beyond basic transcription, this server offers a robust suite of features for deeper content analysis. It can automatically translate video scripts into various languages, generate professional subtitle files in SRT or VTT formats, and produce concise summaries of lengthy recordings. It also features a targeted search capability, enabling users to pinpoint specific mentions of keywords or topics within a video's timeline, which significantly speeds up the research process. For developers and engineers, this tool provides a streamlined way to integrate video data into AI-driven workflows using the Model Context Protocol. Built on Python 3.12, the server supports both stdio and SSE transports, allowing for flexible deployment options through Docker, Smithery, or manual installation. By simply configuring an API key, developers can empower their LLM applications to analyze, translate, and cross-reference YouTube content at scale, effectively turning the world’s largest video platform into a searchable, structured knowledge base.
Category: Design, Media & Creative
Tags: subtitles, summarization, transcription, translation, youtube
bash npx -y @smithery/cli install @brianshin22/youtube-translate-mcp --client claude Manual Installation Requires Python 3.12 or higher. Using uv (recommended): bash uv pip install youtube-translate-mcp Using pip: bash pip install youtube-translate-mcp Docker Installation bash docker build -t youtube-translate-mcp . ---YOUTUBE_TRANSLATE_API_KEY.claude_desktop_config.json file (typically located at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS): Method 1: Local Development (Using uv) json { "mcpServers": { "youtube-translate": { "command": "uv", "args": [ "--directory", "/ABSOLUTE/PATH/TO/youtube-translate-mcp", "run", "-m", "youtube_translate_mcp" ], "env": { "YOUTUBE_TRANSLATE_API_KEY": "YOUR_API_KEY" } } } } Method 2: Docker-based json { "mcpServers": { "youtube-translate": { "command": "docker", "args": [ "run", "-i", "--rm", "-e", "YOUTUBE_TRANSLATE_API_KEY", "youtube-translate-mcp" ], "env": { "YOUTUBE_TRANSLATE_API_KEY": "YOUR_API_KEY" } } } } ---VTT Generation) Problem: Small-scale developers or educational creators need to provide accessible content with subtitles in multiple languages but may not have the budget for professional captioning services. Solution: The MCP can generate industry-standard subtitle files (SRT or VTT) by translating the original transcript into various languages, making it easy to upload them back to YouTube or a private video hosting…
Part of MCP Servers
You can install it automatically for Claude Desktop using the Smithery CLI command `npx -y @smithery/cli install @brianshin22/youtube-translate-mcp --client claude`. Alternatively, install it with Python 3.12 or higher using `uv pip install youtube-translate-mcp` or build and run it with Docker.
The server allows AI assistants to fetch full transcripts of YouTube videos, translate transcripts into different languages, generate subtitle files in SRT or VTT formats, produce structured video summaries, and search for specific keyword mentions across video transcripts.
YouTube Translate MCP supports both the standard stdio transport for local process communication and Server-Sent Events (SSE) transport for network-based setups, which can be run using the `--transport sse` argument on a specified port.
The server requires a valid YouTube Translate API key. You must configure this key by setting the `YOUTUBE_TRANSLATE_API_KEY` environment variable in your client configuration or local environment.
Yes, YouTube Translate MCP is open source and distributed under the MIT license, with source code available on GitHub.