video-tools-mcp
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- AlicenseAqualityBmaintenanceEnables AI agents to analyze videos by producing timestamped transcripts, keyframe contact sheets, and metadata reports from local files or online URLs, fully offline and without API keys.6MIT
- AlicenseNot gradedqualityFmaintenanceExtracts ffprobe metadata, subtitles, scenes, and timelines from video files without frame-by-frame LLM vision, providing evidence-first reading for AI agents.1,063 npm3MIT
- FlicenseAqualityDmaintenanceEnables local media processing (video/audio) using FFmpeg and FFprobe, allowing frame extraction, audio conversion, and metadata retrieval through natural language.5-
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- 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.684 PyPI9MIT
TDQS
Scored across 5 tools
Each tool targets a distinct aspect: metadata, shot detection, frame extraction, keyframe analysis, and context aggregation. There is mild overlap since video_metadata and check_keyframes both report technical video facts, and video_context aggregates the same data the individual tools produce, but purposes remain distinguishable.
All names use snake_case, which is consistent. However, the prefix convention is mixed: detect_shots, extract_frames, and check_keyframes use verb_noun, while video_metadata and video_context use noun_noun, a minor deviation that keeps things readable.
Five tools is well-scoped for a focused video inspection server, with each tool earning its place covering a distinct inspection task plus one aggregation helper.
The surface covers core video inspection: media facts, shots, frames, keyframes, and aggregated context output. Minor gaps exist (e.g. no audio/subtitle extraction or transcript tooling), but these are explicitly deferred to an external service, so agents can work around them.