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get_frame_burst

Read-onlyIdempotent

Extract multiple frames evenly distributed across a video time range for motion and vibration analysis, such as detecting shaking or flickering.

Instructions

Extract multiple frames evenly distributed across a time range.

Designed for motion and vibration analysis where scene-change detection fails because the "scene" doesn't change — only the position/state of objects does.

Example: get_frame_burst(url, "0:15", "0:17", 10) → 10 frames in 2 seconds

  • AI sees the object in different positions across frames → understands the vibration

  • Works for: shaking, flickering, animations, fast scrolling, loading spinners

Supports: Loom (loom.com/share/...), YouTube/Vimeo/TikTok/Instagram/X/Twitch/Dailymotion/Facebook (requires yt-dlp), direct video URLs (.mp4, .webm, .mov), and local video files (absolute path or file:// URI).

Args:

  • url: Video source (URL or local path)

  • from: Start timestamp (e.g., "0:15")

  • to: End timestamp (e.g., "0:17")

  • count: Number of frames (default: 5, max: 30)

Returns: N images evenly distributed between the from and to timestamps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesEnd timestamp (e.g., "0:17")
urlYesVideo source: Loom share link, platform video URL (YouTube, Vimeo, TikTok, Instagram, X, Twitch, Dailymotion, Facebook), direct .mp4/.webm/.mov URL, or absolute path to a local video file
fromYesStart timestamp (e.g., "0:15")
countNoNumber of frames to extract (default: 5)
returnBase64NoReturn frames as base64 inline instead of file paths
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context including supported video sources, the even distribution mechanism, and return format options (file paths or base64), all consistent with annotations.

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

Conciseness5/5

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

The description is highly efficient: a one-line summary, targeted use-case explanation, concrete example, supported platforms list, and parameter breakdown. Every sentence adds value without redundancy.

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?

For a 5-parameter tool with no output schema, the description is comprehensive. It explains the return format (N images), supported video sources, and the algorithmic approach (evenly distributed). Minor omissions like error handling don't detract significantly.

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% with good descriptions. The description adds meaning beyond schema by providing timestamp format examples (e.g., '0:15'), demonstrating usage with an example call, and explaining the 'count' parameter's default and maximum. This contextualizes the parameters for practical use.

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 tool extracts multiple frames evenly distributed across a time range. It distinguishes itself from siblings like get_frame_at and get_frames by specifying the even distribution and targeting motion/vibration analysis.

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 targets motion/vibration analysis where scene-change detection fails, providing clear context for use. However, it does not explicitly mention when not to use this tool or compare to alternatives like get_frame_at or analyze_video.

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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