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Run structured diagnostics on a 3D canvas scene

validate_scene

Analyze a 3D scene HTML file for rendering errors and scene graph issues including floating objects, missing lights, and camera clipping. Returns a structured report with severity and fix suggestions.

Instructions

Loads the scene HTML and returns a JSON report: page/console errors, blank-canvas and detail analysis of the rendered pixels, and — when window.__scene is registered (Three.js) — deep scene-graph checks: floating objects, missing lights, out-of-frustum meshes, NaN transforms, missing UVs/normals, camera clipping, texture problems. Each issue has a severity and a concrete fix suggestion. Fix errors first, then warnings. Call this after render_scene whenever something looks wrong, and at least once before declaring the scene done.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesAbsolute path to a self-contained .html file that renders a 3D scene into a <canvas>
include_screenshotNoAlso return the current-frame screenshot
widthNoViewport width in px
heightNoViewport height in px
settle_framesNorequestAnimationFrame frames to wait after load before capturing
extra_wait_msNoExtra fixed wait after settling, for slow async scenes
timeout_msNoHard cap for the whole operation
Behavior5/5

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

With no annotations, the description fully details the tool's behavior: loads scene HTML, reports page/console errors, performs deep scene-graph checks (floating objects, missing lights, etc.), each with severity and fix suggestion. No hidden side effects.

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?

A single, well-structured paragraph front-loading the main purpose and then detailing checks and usage. Efficiently conveys a lot of information without extraneous text.

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?

Given no output schema and 7 parameters, the description is comprehensive: covers what the tool does, the types of checks, and usage context. It provides enough information for an agent to use it effectively.

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

Parameters3/5

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 context for the overall tool but does not significantly elaborate on individual parameters beyond what the schema provides.

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 defines the tool's purpose: running structured diagnostics on a 3D canvas scene and returning a JSON report. Distinguishes from siblings like 'interact_scene' or 'render_scene' by focusing on validation.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'Call this after render_scene whenever something looks wrong, and at least once before declaring the scene done.' Also provides ordering advice: 'Fix errors first, then warnings.'

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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