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graphgrow

root-ext-cad

by graphgrow

model_build

Idempotent

Run a build123d or CadQuery script to generate a 3D model and export it to STEP/GLB. Returns a build report with volume, area, and center of mass.

Instructions

EXECUTES a parametric CAD script (build123d or CadQuery Python) in this pack's own venv as a resource-capped subprocess (CPU rlimit + wall timeout, default 75 s), then exports the script's module-level result shape to the requested formats (default step+glb) in out_dir — default derived/ beside the script, and never a directory containing the script itself (charter: source is truth, artifacts are derived). PARAMS (your params dict) is available inside the script. Returns the written files and a build report: volume, area, bbox, center of mass, the script's own prints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo
wall_sNo
formatsNo
out_dirNo
script_pathYes
Behavior5/5

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

Beyond annotations that already indicate idempotency and non-destructiveness, the description adds rich behavioral context: it details the subprocess execution, CPU and timeout limits, default export formats, directory constraints ('never a directory containing the script itself'), and the return of a build report. No contradictions with annotations.

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 a single dense sentence that packs significant information. It is front-loaded with the primary action. While every part is relevant, the density slightly harms readability. Breaking into multiple sentences would improve structure.

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 the tool's complexity (executing scripts, exporting, subprocess management), the description covers the core workflow, safety constraints, and return value. It could mention error handling or script failure behavior, but overall it is sufficiently complete for an agent to use 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?

With 0% schema description coverage, the description provides essential meaning for all 5 parameters: it explains script_path is required, params is available inside the script, wall_s defaults to 75, formats default to step+glb, and out_dir defaults to 'derived/' beside the script with discussed constraints. This compensates for the missing schema descriptions, though not with per-parameter detail.

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?

The description clearly states the tool executes a parametric CAD script and exports the result. It uses specific verbs ('EXECUTES', 'exports') and identifies the resource (script, result shape). It distinguishes from siblings like model_inspect and model_validate by focusing on generating geometry from scripts.

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?

The description explains the execution context (subprocess, resource limits) but does not explicitly state when to use this tool versus alternatives like model_inspect or text_to_cad. It implies usage for building models from code, but lacks direct exclusions or recommendations.

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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