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glitch_depth_splatting

Transform video into a 3D particle effect by applying depth-based point splatting using luminance pseudo-depth and scatter parameters.

Instructions

Apply depth-based point splatting effect (requires Node.js + GPU).

Extracts pseudo-depth from luminance and renders the image as scattered points, creating a 3D particle-like appearance.

Args: input_path: Absolute path to input video. output_path: Absolute path for output video. depth_scale: Depth extraction intensity. Default 1.0. spread: Point spread distance in pixels. Default 10.0. point_size: Size of each splatted point. Default 3.0. threshold: Depth cutoff threshold (0-1). Default 0.5.

Returns: Dict with success status and output_path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_pathYes
output_pathNo
depth_scaleNo
spreadNo
point_sizeNo
thresholdNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries full burden. Explains that it extracts pseudo-depth from luminance and renders points, and returns a dict with success status and output_path, implying non-destructive output. Lacks explicit statement about side effects or file modification.

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?

Concise and well-structured: overview sentence, elaboration, then clear Args list with defaults, and Returns. No wasted words.

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

Completeness5/5

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

Covers all parameters, return format, prerequisite, and overall behavior. Despite having an output schema, the description provides enough context for an agent to understand input, output, and constraints for a complex effect tool.

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

Parameters5/5

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

Schema has 0% coverage, but description adds meaningful explanations for all 6 parameters including defaults and a range for threshold, significantly enhancing understanding beyond the schema's bare types.

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 it applies a depth-based point splatting effect, specifies the resource (video), and distinguishes from many sibling glitch tools by mentioning pseudo-depth and 3D particle-like appearance.

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?

Mentions a prerequisite (Node.js + GPU) but does not explicitly state when to use this tool versus the many other glitch effects available in the sibling list. Usage context is implied but not guided.

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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