My AI agent learned to watch videos this week. Here is the 3-line version of how to give yours the same ability.
claude-real-video (MIT, 1.9k stars) extracts scene-aware, deduplicated keyframes plus a timestamped transcript from any video URL or local file — all processing on your machine. Since 0.8.0 it ships as an MCP server, so MCP clients can request a video directly.
Install
pip install 'claude-real-video[mcp]'
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Claude Code:
claude mcp add crv -- crv-mcp
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Claude Desktop — add to claude_desktop_config.json:
{ "mcpServers": { "crv": { "command": "crv-mcp" } } }
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Cursor and any other MCP client: same stdio command, crv-mcp.
What you get
Two tools show up:
-
watch_video(source, max_frames, language, transcribe)— downloads (URL) or reads (local file), extracts keyframes at scene changes, drops near-duplicates, transcribes with Whisper, and returns the transcript plus the first batch of frames as images straight into the conversation. -
get_frames(source, start_index, count)— pages through the rest of the frames.
Analyses are cached per source under ~/.cache/crv-mcp, so a follow-up question about the same video is instant.
Why scene-aware instead of 1 fps
Fixed-interval sampling wastes context on near-identical frames and still misses fast cuts. Scene detection keeps the frames that actually differ: a 58-second test clip goes from 58 sampled frames to 26 that matter. Fewer tokens, nothing missed. The benchmark folder in the repo has the full comparison, reproducible.
Honest notes
- Verified end-to-end on Claude Code (a real model run described a test video’s frames correctly through the tool). Claude Desktop and Cursor speak the same MCP stdio protocol — config above; open an issue if anything misbehaves.
- Transcription needs Whisper (
pip install 'claude-real-video[whisper]'or[fast]); frames work without it. - The mcp SDK shipped 2.0 on July 28 and moved FastMCP — 0.8.0 dual-imports both majors, tested separately on each. If you build your own MCP server this month, test in a clean venv.
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