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crop_video

Crop videos to specific dimensions and positions using FFmpeg. Specify width, height, and offset coordinates to extract desired portions from video files.

Instructions

Crop a video using ffmpeg-python.

Params: input_video_path: Path to input video (required) safe_crop: If True, allows exact cropping (ignores mod-2 restrictions). Default: False height: Output height (default: 480) width: Output width (default: 640) x_offset: Top-left X coordinate of crop (default: 0) y_offset: Top-left Y coordinate of crop (default: 0)

Returns: str: Path to the cropped video.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_video_pathYes
safe_cropNo
heightNo
widthNo
x_offsetNo
y_offsetNo
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'crops a video' (implying mutation) and returns a path, but lacks details on permissions, side effects (e.g., file overwriting), error handling, or performance considerations like processing time or resource usage.

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 well-structured with clear sections for 'Params' and 'Returns', making it easy to parse. It's appropriately sized, with each sentence adding value, though the 'Returns' section could be slightly more detailed given the lack of output schema.

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

Completeness3/5

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

Given 6 parameters, no annotations, and no output schema, the description does a decent job covering parameter semantics but lacks behavioral context (e.g., how cropping interacts with video properties). It's adequate for basic use but leaves gaps in error handling and integration with sibling tools.

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 description coverage is 0%, so the description must compensate. It provides clear semantics for all 6 parameters, explaining their purposes, defaults, and requirements (e.g., 'input_video_path' as required, 'safe_crop' as a boolean with a helpful note). This adds significant value beyond the bare schema.

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

Purpose4/5

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

The description clearly states the tool's purpose with a specific verb ('crop') and resource ('video'), and mentions the implementation technology ('using ffmpeg-python'). However, it doesn't explicitly differentiate from sibling tools like 'clip_video' or 'scale_video', which might have overlapping functionality.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like 'clip_video' or 'scale_video'. The description lacks context about typical use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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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