yt-analysis-mcp
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- AlicenseAqualityCmaintenanceAnalyzes YouTube videos using Google's Gemini API, allowing users to get summaries or ask questions about video content via direct URL input.5192MIT
- AlicenseNot gradedqualityDmaintenanceAnalyzes public YouTube videos with Google Gemini, enabling summarization, timestamped insights, metadata extraction, audio analysis, and long-video sessions with follow-up questions.194MIT
- AlicenseBqualityDmaintenanceEnables conversational analysis of YouTube videos using Gemini 2.5 Pro, supporting multi-turn sessions, direct URL processing, and local video uploads.101MIT
- FlicenseBqualityDmaintenanceEnables AI-powered YouTube video analysis including transcript management, video summaries, chapter generation, keyword extraction, and playback control. Supports searching videos, retrieving channel/playlist information, and translating transcripts using Google Gemini AI.14-
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to analyze YouTube videos by extracting summaries, timestamped outlines, diagrams with Mermaid code, and targeted insights about specific moments using Gemini's native video understanding.MIT
- FlicenseNot gradedqualityDmaintenanceEnables LLMs to interact with YouTube videos by fetching transcripts, summarizing content, and answering questions based on video context.-
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose with no ambiguity: ask_about_video answers questions about content, extract_frames extracts frames at user-provided timestamps, extract_screenshots uses AI to find and extract key frames, get_video_timestamps previews AI-identified timestamps, and summarize_video provides text summaries. The descriptions explicitly differentiate their use cases, such as distinguishing between extract_frames (manual timestamps) and extract_screenshots (AI-selected timestamps).
All tool names follow a consistent verb_noun pattern with underscores, using clear action verbs like ask_about, extract, get, and summarize paired with descriptive nouns like video, frames, screenshots, and timestamps. There are no deviations in style or convention, making the set predictable and easy to understand at a glance.
With 5 tools, the count is well-scoped for the YouTube analysis domain, covering core workflows like content querying, frame extraction (both manual and AI-driven), timestamp previewing, and summarization. Each tool earns its place without redundancy, and the number is manageable for agents to navigate effectively.
The tool set provides strong coverage for video analysis, including content understanding, visual extraction, and summarization, with no dead ends. A minor gap exists in lacking direct video metadata retrieval (e.g., title, duration, uploader), but agents can work around this by using existing tools like ask_about_video or summarize_video for such information.