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pzfreo

build123d-mcp

design_audit

Read-only

Audit a CAD program's numeric parameters by perturbing each and rebuilding to check validity, identifying brittle parameters that break under small edits.

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?

Disclosures are consistent with readOnlyHint annotation, stating live session is never mutated. Details subprocess execution, wall-clock budget, partial reports, and definitions of 'brittle' and 'inconclusive'. Provides high behavioral transparency beyond 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?

Description is dense but well-organized: starts with purpose, then process, then output format, definitions, limitations. Every sentence adds value; no redundancy. Could be slightly more concise but front-loaded effectively.

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 presence of output schema, description provides complete context: input parameters, process, return format with fields, known limitations, and edge cases. No gaps remain for an AI agent to understand tool behavior.

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?

Despite 0% schema description coverage, the description explains both parameters: epsilon as relative nudge (0<epsilon<1, default 10%) and max_params as cap on parameters audited. Adds meaning beyond schema defaults.

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?

Description clearly states the tool audits a session program as a design, surfaces numeric parameters, and tests robustness. It distinguishes from siblings like `script` or `validate` by its specific focus on parameter robustness analysis.

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?

Explicitly describes when to use (to audit design robustness) and includes limitations (only literal-valued top-level names, budget constraints). Advises hoisting if no parameters found. Could be more explicit about when not to use compared to specific siblings, but still strong.

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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