MCP server for video analysis — extracts transcripts, key frames with OCR, and annotated timelines from video URLs. Supports Loom and direct video files (.mp4, .webm). Zero auth required.
An MCP server for image and video processing that allows AI assistants to resize, convert, compress, and analyze media files locally without API keys. It supports a wide range of formats and provides tools for tasks such as metadata removal, frame extraction, and video conversion.
Provides image and video analysis capabilities for LLMs, with local preprocessing (ffmpeg/OpenCV) and any OpenAI-compatible vision model for understanding and Q&A.
Lets 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.
Extracts ffprobe metadata, subtitles, scenes, and timelines from video files without frame-by-frame LLM vision, providing evidence-first reading for AI agents.
Enables 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.
Let AI agents watch videos: local transcripts, speaker labels, scenes, chapters and exact-moment search from any video URL or file. Fully local, no API keys.
Self-hosted video intelligence pipeline that turns video into structured, citable, queryable JSON scene graphs, enabling AI assistants to analyze and search across video libraries without re-processing.