YouTube Video Summarizer MCP Server
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- AlicenseNot gradedqualityDmaintenanceExtracts captions, metadata, and descriptions from YouTube videos to enable AI assistants to summarize their content.6 npmMIT
- AlicenseAqualityDmaintenanceEnables AI assistants to extract YouTube video metadata, subtitles, and top comments without downloading videos.36MIT
- AlicenseCqualityDmaintenanceEnables AI assistants to research YouTube videos by collecting captions, comments, and channel information for analysis and comparison.109MIT
- FlicenseNot gradedqualityDmaintenanceEnables YouTube data crawling and AI-powered summarization of videos, including channel metadata retrieval, time-range queries, and automatic transcription with multi-language support.-
- FlicenseDqualityDmaintenanceEnables AI assistants to fetch and analyze transcripts from YouTube videos using video IDs or URLs, with support for multiple language preferences.1-
- AlicenseAqualityDmaintenanceEnables AI assistants to fetch YouTube video transcripts with precise timestamps, multi-language support, and time-range filtering.31MIT
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as retrieving video details and captions for summarization from a YouTube URL.
Since there is only one tool, naming consistency is inherently perfect. The tool name uses a clear verb-noun pattern (get-video-info-for-summary-from-url) that is descriptive and follows a logical structure.
A single tool is too few for a server named 'YouTube Video Summarizer MCP Server', as it suggests a broader summarization functionality. The tool only fetches video info and captions, lacking operations like generating summaries, processing text, or managing multiple videos, making the scope feel incomplete and thin.
The tool set is severely incomplete for the server's purpose. It only provides video details and captions, but does not include any summarization tools (e.g., generate-summary, analyze-captions) or related operations (e.g., list-videos, save-summary), leaving obvious gaps that will hinder agent workflows.