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pzfreo

build123d-mcp

analyze_printability

Read-only

Checks a 3D shape for FDM printability issues like overhangs, thin walls, and bed adhesion, returning a detailed report with severity and location.

Instructions

Analyse a build123d shape for FDM printability using augura (BREP-exact analysis).

Checks: overhangs, manifold/watertight, tip-over risk, brim/raft need,
minimum vertical feature (→ max layer height), and thin walls. Optionally
checks bed-fit against a declared build volume.

Returns a plain-text summary followed by a JSON report with per-finding
detail (kind, severity, message, area/location where applicable).

object_name: named object from show() (default: current shape).
support_angle: faces shallower than this many degrees from horizontal need
    support (default 45).
nozzle: nozzle diameter in mm for wall-thickness check (default 0.4).
min_perimeters: walls thinner than min_perimeters × nozzle are flagged
    (default 2).
build_volume: optional build envelope as 'X Y Z' in mm, e.g. '256 256 256';
    omit to skip the bed-fit check.
bed_tol: Z tolerance in mm for identifying bed-contact faces (default 0.001);
    raise it for parts whose bottom faces sit slightly off Z=0.
min_feature: minimum vertical feature size in mm to flag (default 0.5).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nozzleNo
bed_tolNo
min_featureNo
object_nameNo
build_volumeNo
support_angleNo
min_perimetersNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations indicate readOnlyHint=true, which is consistent with the description (no mention of side effects). The description details the output format (plain-text summary + JSON report) and explains the behavior of each parameter, adding 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?

The description is well-structured with an introductory sentence, a bullet-like list of checks, output format, and parameter details. It is somewhat lengthy but every sentence adds value. Minor redundancy could be trimmed, but overall it is clear and organized.

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 tool complexity (7 parameters, output schema present, no required params), the description covers all aspects: purpose, checks, output, and detailed parameter explanations. It provides sufficient context for an AI agent to invoke the tool 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 description coverage is 0%, so the description fully compensates by explaining each parameter's purpose, default, and format (e.g., build_volume as 'X Y Z' string). It adds clear semantic meaning that the schema alone lacks.

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's purpose: analyzing a build123d shape for FDM printability using augura. It lists specific checks (overhangs, manifold/watertight, etc.), distinguishing it from sibling analysis tools like design_audit or health_check which have different scopes.

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?

The description outlines when to use the tool (for FDM printability analysis) and lists the checks performed. It does not explicitly mention when not to use it or alternatives, but the specialized focus implies appropriate usage. It provides enough context for an AI agent to decide.

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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