---
name: video-perception
description: Use when the user mentions a video file (.mp4, .mov, .avi, .mkv, .webm), a YouTube URL, asks to watch/analyze/review a video, or references video content in conversation
---

# Video Perception

You have access to video understanding tools via the claude-video-vision MCP server.

## Available Tools

- `video_analyze` — Analyze video structure with ffmpeg filters (scene changes, silence, motion, etc.). Use this BEFORE extracting frames to plan your strategy.
- `video_watch` — Extract frames + process audio from a video. Supports variable FPS/resolution per segment.
- `video_detail` — Drill into specific segments. Separates extraction from viewing — extract many frames, view few at a time.
- `video_info` — Get video metadata without processing.
- `video_configure` — Change settings (backend, resolution, enable_index, etc.).
- `video_setup` — Check/install dependencies.

## Workflow

**IMPORTANT: You MUST follow these steps in order. Do NOT skip step 2.**

1. Always start with `video_info` to get duration, resolution, and audio presence.
   If the user gives a YouTube URL, pass the URL directly as `path`.
   The MCP server downloads it with `yt-dlp`, prefers YouTube subtitles/auto-captions
   for transcription, and falls back to the configured audio backend only when
   captions are missing, empty, or suspiciously incomplete.

2. **REQUIRED for videos > 30s:** Call `video_analyze` BEFORE extracting any frames.
   This is NOT optional — it gives you structural data to make smart extraction decisions.
   Select filters relevant to the user's question:

   | User intent | Filters to select |
   |---|---|
   | "What happens in this video?" | scene_changes, silence, transcription |
   | "Find the scene transitions" | scene_changes, black_intervals |
   | "Are there frozen/stuck parts?" | freeze, blur |
   | "Is this a talking head or action?" | motion |
   | "When does the music start?" | silence, loudness |
   | "Analyze the lighting" | exposure |
   | "Summarize this lecture" | transcription, scene_changes, silence |
   | General / unclear intent | scene_changes, silence, transcription |

   Always include `transcription: true` when the video has audio — the transcription
   tells you WHERE to look visually.

   `scene_changes: true` reports hard cuts (scdet score >= 8). If the user needs softer
   transitions (dissolves, slow fades), pass `scene_changes: { threshold: 4 }`; if
   handheld or fast-moving footage floods the list, raise it (e.g. `{ threshold: 15 }`).

3. Use the analysis results and transcription to plan your frame extraction strategy:
   - Low FPS (0.1-0.5) for static or predictable segments
   - Higher FPS (1-3) only around scene changes, motion peaks, or moments
     referenced in speech ("look at this", "as you can see", "let me show you")
   - Never exceed the minimum FPS needed for the task
   - Prefer fewer segments at lower FPS — you can always drill deeper

4. Call `video_watch` to extract frames:
   - For **short videos (< 2 minutes):** Use `fps: "auto"` without `view_sample` — short videos need full coverage to avoid missing brief moments. The auto FPS already adapts to duration.
   - For **long videos (> 2 minutes):** Use `segments` based on analysis data with variable FPS, and `view_sample` to limit initial frame count. You can always drill deeper with `video_detail`.

5. Use `video_detail` to drill into specific moments:
   - Start with 3-5 second windows around points of interest
   - Use `view_sample: 3` to preview (first, middle, last frame)
   - Then request specific timestamps with `view` if you need more detail
   - Expand the window only if the initial view is insufficient
   - Treat frame viewing like a binary search — narrow down to what matters
   - Never view all extracted frames at once

6. When the user asks follow-up questions about the same video, consult
   the manifest already in your context. Do not re-extract frames you
   already have at the same resolution. Do not re-request frames you
   already have in context.

## Parameter Guide

**fps:** `"auto"` for general overview. Use the video's original fps (from `video_info`) for frame-by-frame detail. Use 5-10 for analyzing specific short moments. Use 0.1-0.5 for long videos.

**resolution:** 256-512 for quick scans. 512-768 for normal analysis. 1024+ when reading on-screen text or fine details.

**segments:** Use when you have analysis data. Each segment can have its own fps and resolution. Overrides global fps/start_time/end_time.

**view_sample:** Returns N evenly spaced frames from the extracted set. Use this to avoid flooding context with too many images.

**skip_audio:** Set to true when you only need visual analysis.

**YouTube URLs:** Pass supported YouTube URLs directly as `path`. Treat
`transcription_source: "youtube_subtitles"` as stronger than
`youtube_auto_captions`; auto-captions can still have recognition errors.

## Working with Results

You receive:
- **Manifest** (when enable_index is on) — index of all cached frames by resolution and timestamp. Use this to avoid redundant requests.
- **Frames** as images — look at them to understand what's happening visually
- **Audio transcription** with timestamps — read the speech content
- **Audio tags** — non-speech events (music, sounds, etc.)
- **Analysis data** — scene changes, silence intervals, motion levels, etc.

Combine all sources to form a complete understanding. Use analysis + transcription to guide where you look visually. The analysis tells you WHEN things happen; the frames tell you WHAT happens.