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analyze_moment

Read-onlyIdempotent

Analyze a specific video segment between two timestamps: extract frames with OCR, filter transcript, and generate an annotated timeline to understand exactly what happens on screen and audio.

Instructions

Deep-dive analysis of a specific time range in a video.

Combines burst frame extraction + transcript filtering + OCR + annotated timeline for a focused segment of the video.

Use this when you need to understand exactly what happens between two timestamps:

  • What's on screen (frames + OCR text extraction)

  • What's being said (transcript filtered to the range)

  • Unified timeline merging visual and audio content

Example: analyze_moment(url, "1:30", "2:00", 10) → 10 frames + transcript + OCR for that 30s window

Supports: Loom (loom.com/share/...) and direct video URLs (.mp4, .webm, .mov). Requires video download capability for frame extraction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesVideo URL (Loom share link or direct mp4/webm URL)
fromYesStart timestamp (e.g., "1:30")
toYesEnd timestamp (e.g., "2:00")
countNoNumber of frames to extract in the range (default: 10)
ocrLanguageNoTesseract OCR language codes (default: "eng+por"). Use "+" to combine: "eng+spa", "eng+fra+deu".
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds context about video download requirement, supported sources, and the combined output (frames, transcript, OCR, timeline), which enriches transparency.

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?

The description is front-loaded with purpose and structured logically. It contains 5 sentences plus an example, which is concise for the complexity involved, though a bit verbose.

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 no output schema and the tool's complexity (combining multiple analyses), the description provides a solid overview of what the tool returns (frames, transcript, OCR, timeline) and the capabilities, which is adequate for an agent to decide.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value with an example showing how count parameter works and explains the combined behavior of the parameters, making it more than a repetition of schema.

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?

The description clearly states the verb 'analyze' and the resource 'a specific time range in a video', and distinguishes it from siblings by emphasizing it combines burst frame extraction, transcript filtering, OCR, and annotated timeline for a focused segment.

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

Usage Guidelines4/5

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

The description explicitly says 'Use this when you need to understand exactly what happens between two timestamps' and provides an example. It does not explicitly exclude alternative uses but is sufficiently clear about its intended use case.

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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