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viral_clip_extractor

Analyze video audio to detect high-energy moments, then extract them as platform-optimized clips using hardware-accelerated encoding.

Instructions

Analyze a video to find the most engaging viral moments and extract them as clips. Uses librosa RMS energy analysis + Non-Maximum Suppression for moment detection, then VideoToolbox hardware-accelerated encoding for frame-accurate clipping.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
video_pathYesAbsolute path to the input video file (.mp4, .mov, .webm)
clip_durationNoTarget duration for each clip in seconds (5, 10, 15, 30, or 45)
num_clipsNoNumber of top viral moments to extract (default 3)
output_dirNoOutput directory for clips (defaults to video's directory)
platformNoTarget platform for aspect ratio (tiktok, reels, shorts, story, feed, twitter, youtube, original)original
start_timeNoManual override — if >= 0, extracts a single clip starting at this second (bypasses AI scan)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Discloses algorithms (librosa RMS, NMS, VideoToolbox) and manual override. Missing details on failure cases or resource usage, but covers key behavioral aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, first states purpose clearly. Second adds technical detail. Slightly verbose with implementation specifics, but overall concise and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, 1 required, and presence of output schema, description covers the main workflow and overrides. Could mention output format, but output schema compensates.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. Description adds no extra semantics beyond the schema; technical details are not linked to parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the verb 'analyze' and 'extract' with the resource 'video clips' for viral moments. Distinguishes from siblings like produce_video by specifying algorithmic approach and purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance. The description implies it's for extracting viral clips but doesn't contrast with alternatives or mention limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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