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model_look

Render orthographic front, right, and top views plus a three-quarter view of a Three.js modelling scene, and inspect each object's transform, material, and mesh statistics.

Instructions

Shows the modelling scene that model_run builds: one picture of it (or of object) from the front, right and top (orthographic, so sizes read true against the grid) and a three-quarter view, under a neutral studio light with a ground grid whose step it states, and one line per object: name, type, place, turn and scale, material (colour, roughness, metalness) and, for a mesh, triangles, size, whether it is closed, its volume and genus. With object, that object in full; with cameras, those views instead of the four.

Input Schema

NameRequiredDescriptionDefault
cwdYesThe game project's folder.
sizeNoThe picture's side in pixels (1024 by default, at most 1568).
objectNoThe name of an object in the modelling scene: frame the picture to it and list it in full.
camerasNoUp to 9 views in place of the four, each { label, position, lookAt } in the scene's metres (lookAt defaults to the middle of what is framed).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden and describes the rendered output in detail (lighting, grid, orthographic projections, object listing fields). It does not explicitly state that the operation is read-only or mention any side effects, but 'Shows' strongly implies a safe inspection.

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 one long, dense sentence plus a short second sentence. It is front-loaded with the core action and every detail seems purposeful, though the structure could be clearer with punctuation or bullets for such a feature-rich tool.

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 the absence of an output schema, the description fully explains what the tool returns: pictures and a textual per-object listing. It covers the effect of each optional parameter and the default view set, leaving no major gaps for an agent to call it correctly.

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%, so baseline is 3, but the description adds meaning: `object` frames and lists that object in full, and `cameras` replaces the default four views with custom ones. The `size` parameter is not elaborated, but the schema already documents it.

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?

States a specific verb (shows) and resource (the modelling scene built by model_run), including what is returned: orthographic views, a three-quarter view, and a per-object listing. It is immediately distinguishable from siblings like model_audit or model_run.

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?

Clearly implies usage after model_run builds the scene, and explains how the `object` and `cameras` parameters change the view. It does not explicitly exclude alternatives or state when not to use it, but the context is clear.

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