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pzfreo

build123d-mcp

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Idempotent

Evaluate a selector expression on a named CAD object to retrieve geometry details such as type, area, center, or normal. Returns a structured descriptor with reference label.

Instructions

Evaluate a selector expression against a named object and return a geometry descriptor. selector is a Python expression suffix applied to the object, e.g. '.faces().filter_by(Axis.Z).last()'. If label is given, the descriptor is stored in session.geometry_refs[label] and appears in session_state(). Returns JSON: {label, ref, object, selector, type, area/length, center, normal (for Face)}. The ref field uses @cad[object#label] format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelNo
selectorYes
object_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already indicate idempotentHint=true and readOnlyHint=false, but the description adds useful behavioral context: it stores results in session.geometry_refs if a label is provided and describes the return format. This goes beyond the annotations without contradicting them.

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?

Three sentences pack substantial information: purpose, example usage, return format, and side-effect of labeling. While concise and front-loaded, the structure could be more bulleted or segmented for easier parsing.

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 presence of an output schema (not shown but noted), the description covers the essential purpose, parameter usage, and return structure. It omits details about error cases or default behavior when label is omitted, but overall suffices for typical use.

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%, so the description carries the full burden. It explains the selector parameter with an example ('.faces().filter_by(Axis.Z).last()'), clarifies that object_name is a named object, and describes the label's storage effect. This significantly compensates for the lack of schema descriptions.

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 evaluates a selector expression against a named object and returns a geometry descriptor. It provides a specific verb ('evaluate') and resource, distinguishing it from sibling tools like 'measure' or 'inspect_drawing' that perform different analyses.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives like 'measure' or 'clearance'. It provides no context about prerequisites, when not to use it, or how it compares to other geometry query tools, leaving the agent to infer usage solely from the purpose.

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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