mcp-video-learn
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- AlicenseNot gradedqualityBmaintenanceEnables any LLM to watch videos by providing scene-aware keyframes, storyboards, and timestamped transcripts through tools for opening videos, fetching frames, and retrieving transcript segments.MIT
- AlicenseAqualityAmaintenanceEnables AI clients to watch local video files or YouTube/Bilibili and other supported URLs, receiving timestamped transcripts, subtitles, searchable text and keyframe contact sheets. Runs fully offline with local speech recognition and bundled ffmpeg, so no API key or cloud upload is needed.674 PyPI13MIT
- AlicenseNot gradedqualityCmaintenanceEnables users to turn GIFs, local videos, and Bilibili videos into timestamped, queryable, and verifiable frame evidence and event timelines, while passing extracted frames to the current session's model instead of calling an external vision API.MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to watch YouTube videos by extracting frames at scene changes and visual references, pairing each frame with the exact words spoken at that timestamp. Provides dense frame-transcript interleaving for any model.32 npm5MIT
- AlicenseNot gradedqualityAmaintenanceLets any LLM agent actually watch videos: a watch_video tool takes a URL or local file and returns scene-aware keyframes fused with a timestamped transcript, processed 100% locally with per-source caching.1,045 PyPI2,275MIT
- AlicenseAqualityCmaintenanceEnables coding agents to turn videos from social platforms or local files into a small set of distinct frames plus a manifest, so they can answer visual questions by reading image paths. Also provides metadata lookup for captions and authors without downloading the video.2MIT
TDQS
Scored across 3 tools
bili_video_info is clearly a metadata-only check, but bili_video_watch and media_frames both accept Bilibili links and both return frame grids, creating real overlap. The descriptions do try to separate them (talking-head/subtitle use case vs. generic animated media), so an agent can usually choose correctly, but the boundary requires reading carefully.
Two tools share a bili_video_ resource prefix while the third uses a generic media_ prefix, and the verb/action component varies (info = noun, watch = verb, frames = noun). Readable and domain-suggestive, but no predictable verb_noun pattern across the set.
Three tools is on the lean side but each covers a distinct stage (triage metadata → deep watch → generic media inspection), so nothing feels redundant filler. The scope is intentionally narrow, so a small count is defensible.
The core workflow—decide whether to watch, then extract frames plus subtitles, plus a generic media path—is covered end to end. Gaps remain: no subtitle/transcript-only retrieval, no timestamp-targeted frame extraction, and pure-speech content is explicitly unsupported, which will dead-end some 'what does this say' queries.