@minbang930
基本情報
MCP (Model Context Protocol) server that utilizes the Google Gemini Vision API to interact with YouTube videos.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"youtube-vision": {
"command": "npx",
"args": [
"-y",
"youtube-vision"
],
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY",
"GEMINI_MODEL_NAME": "gemini-2.0-flash"
}
}
}
}Generates a summary of a given YouTube video URL using Gemini Vision API.
Answers a question about the video or provides a general description if no question is asked.
Extracts key moments (timestamps and descriptions) from a given YouTube video.
Lists available Gemini models that support the 'generateContent' method.
Youtube Vision is an MCP (Model Context Protocol) server that uses the Google Gemini Vision API to analyze YouTube videos. It allows users to get descriptions, summaries, answer questions, and extract key moments from videos. Designed for developers integrating YouTube video analysis into AI-powered workflows.
Install via Smithery (npx -y @smithery/cli install @minbang930/youtube-vision-mcp) or run directly with npx -y youtube-vision. Configure your MCP client (e.g., Claude Desktop) with the command and environment variables: GEMINI_API_KEY (required) and optionally GEMINI_MODEL_NAME. You can then use its four tools (ask, summarize, extract key moments, list supported models) by providing a YouTube URL and optional parameters.
generateContentYoutube Vision directly integrates the Google Gemini Vision API into the MCP ecosystem, allowing you to ask questions, summarize, and extract moments from YouTube videos through a simple tool interface. It does not rely on YouTube transcripts but uses visual analysis via Gemini.
You need Node.js version 18 or higher and a Google Gemini API key from Google AI Studio or Google Cloud Console. The server runs locally via stdio.
The README does not specify data storage. Video analysis is performed on-demand through the Gemini API, and generated content is returned to the MCP client. Users are responsible for complying with Google’s data usage policies.
The README does not list specific rate or usage limits. It notes that model versions marked as “Experimental” or “Preview” are not permitted for production use per Gemini API terms.
It communicates via stdio transport and authenticates using the `GEMINI_API_KEY` environment variable, which must be set in the MCP client’s configuration.
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