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Render a mesh in a studio

render_mesh

Render a mesh from six studio angles to judge its shape before building on it. Creates centred, floor-lit PNG views from both ends, sides, and 3/4 perspectives.

Instructions

Render a mesh from six studio angles: both ends, both sides and two 3/4 views. The mesh is centred, put on the floor and lit with a key, a fill and a rim light. Use it to judge the shape before building anything on top of it. Needs Blender; set HF_ASSETS_BLENDER if it is not on PATH.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
glbYesThe mesh to render, as a path.
widthNoRender width in pixels.
heightNoRender height in pixels.
lengthNoLength of the longest horizontal axis in metres.
prefixNoFile name prefix.render
out_dirNoWhere to write the PNGs.out/renders
samplesNoCycles samples per view; lower is faster.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
logYesWhat the engine printed while it worked.
filesYesEverything that was written.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose real behavioral traits: the fixed six-view framing, centring/floor placement, three-light setup, that it writes PNGs, and an external dependency ('Needs Blender; set HF_ASSETS_BLENDER'). It could still note that it is a read-only, non-destructive operation, but the coverage is solid.

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?

Four sentences, front-loaded with what it renders before moving to setup and prerequisites. Every sentence carries information (angles, lighting, purpose, dependency). Slightly dense but no wasted filler.

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?

Given an output schema exists, return values need not be explained, and the description covers purpose, visual setup, and the Blender prerequisite. The one gap is that it never distinguishes this tool from the sibling model_sheet, which likely also produces multi-view renders, leaving that routing decision unclear.

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 the baseline is 3, and the description adds genuine meaning on top: the six-angle framing explains why width/height matters, the floor/centring context informs 'length', and the PNG output clarifies out_dir. It stops short of documenting syntax for any single parameter, keeping it just above baseline.

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 (render) and resource (mesh) and further specifies the output: six studio angles (both ends, both sides, two 3/4 views). This is enough to distinguish it from generate_mesh and the other generation siblings without opening a schema.

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?

It gives a clear usage context: 'Use it to judge the shape before building anything on top of it.' That tells the agent when this tool fits. It does not name alternatives such as model_sheet, so exclusions are left to inference rather than stated.

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