Skip to main content
Glama
KyaniteLabs

kinocut

by KyaniteLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

glitch_point_cloud

Apply a 3D point-cloud rendering effect to video by sampling frames into scattered points with adjustable density, point size, rotation, and depth displacement.

Instructions

Apply point cloud rendering effect (requires Node.js + GPU).

Samples the image as scattered points arranged in a 3D-rotated grid, with depth-based displacement creating a volumetric look.

Args: input_path: Absolute path to input video. output_path: Absolute path for output video. density: Point sampling density (0-1). Default 0.5. point_size: Size of each point. Default 2.0. rotation: 3D rotation angle in degrees. Default 0.0. depth: Depth displacement intensity. Default 1.0.

Returns: Dict with success status and output_path.

Input Schema

NameRequiredDescriptionDefault
depthNo
densityNo
rotationNo
overwriteNoExplicitly allow replacing existing media. Existing non-media files are protected.
input_pathYes
point_sizeNo
output_pathNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.16.4
    • addedInput schema / properties / overwrite
      Added value: +{
+  "default": false,
+  "description": "Explicitly allow replacing existing media. Existing non-media files are protected.",
+  "title": "Overwrite",
+  "type": "boolean"
+}
  2. Changed1 schema field changedv1.16.0
    • addedInput schema / additionalProperties
      Added value: +false
  3. Addedv1.5.1

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the transparency burden and does disclose the GPU/Node.js requirement, the visual transformation, and a basic return shape. It does not explain overwrite behavior, performance characteristics, or whether the input is modified, leaving meaningful gaps for a video mutation tool.

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 and organized into Args and Returns sections, making it easy to scan. The Returns section is somewhat redundant because an output schema exists, but it is short and does not significantly dilute the useful content.

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 moderately complex GPU-based effect with seven parameters and an output schema, the description provides enough detail to invoke the tool correctly, including parameter semantics and prerequisites. The missing pieces are mainly overwrite behavior and sibling differentiation, which are secondary to calling the tool.

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 only 14%, so the description must compensate, and it does: it documents input/output paths and gives meaning, ranges, units, and defaults for density, point_size, rotation, and depth. It misses the overwrite parameter, but that parameter is already described in the 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 names a specific verb and effect: apply a point cloud rendering effect, and explains the visual result as scattered points in a 3D-rotated grid with depth displacement. It is clear what the tool does, but it does not distinguish this effect from nearby siblings such as glitch_depth_splatting or other glitch tools.

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?

Usage is implied by the effect name and visual description, and the Node.js + GPU requirement gives an important operational precondition. However, there is no explicit guidance on when to choose this over alternative glitch effects or when not to use it.

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

Deploy Server

Other Tools