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pzfreo

build123d-mcp

design_audit

Audits a 3D design program by extracting numeric parameters and testing their robustness to small edits, identifying brittle parameters that fail to rebuild or pass validity checks.

Instructions

Audit the current session program as a design, not just a shape: surface its named numeric parameters (Θ) and test how robust each is to editing. Parses the assembled program (see script()) for top-level numeric assignments (e.g. plate_thickness = 5.0), then rebuilds the program with each parameter nudged ±epsilon (default ±10%) in a hard-bounded subprocess (the live session is never mutated) and runs the validity gate on each result. Returns JSON: {parameters, baseline, audit:[{name, value, perturbations:[{delta_pct (realized), new_value, discrete_step?, rebuilt, passes_gate, volume_delta_pct, reasons?}], brittle}], summary:{robust, brittle, inconclusive, ...}, note}. A parameter is brittle if a small change fails to rebuild or drops below the validity gate — the thin-wall / coordinate-reasoning failure mode where a valid shape is not an editable design (Arko-T §6); a parameter reassigned at the top level is inconclusive (perturbation is overwritten), not counted as robust. If no named parameters are found, the program uses inline magic constants and the note advises hoisting them to a parameter block. Known limitation: only literal-valued top-level names are surfaced as Θ — a derived parameter (radius = diameter / 2) is not listed, though perturbing its upstream literal flows through. Bounded by a wall-clock budget and max_params (returns a partial report rather than risking a timeout). epsilon: relative nudge, 0<epsilon<1. max_params: cap on parameters audited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
epsilonNo
max_paramsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations, so the description fully carries the burden. It discloses no mutation of live session, hard-bounded subprocess, definition of 'brittle'/'inconclusive', known limitation on derived parameters, and bounded by budget/max_params.

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?

Description is well-structured and front-loaded. It is dense but each sentence adds value; slight room for conciseness but overall effective.

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 low complexity (2 params, no required, no enums), description covers behavior, output structure, limitations, and boundary conditions. Output schema existence reduces burden, but description already thorough.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% but description compensates fully: explains epsilon (relative nudge, 0<epsilon<1, default ±10%) and max_params (cap, partial report on exceed). Adds meaning beyond 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?

Description clearly states the tool audits the session program for parameter robustness, with specific actions (parse, perturb, validate). It distinguishes the tool's unique function but does not explicitly differentiate from sibling 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?

Description provides implied usage for auditing parameter robustness and mentions limitations, but no explicit when-to-use or when-not-to-use compared to alternatives.

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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