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list_faces

Enumerate faces of solid bodies, returning centroid, type, and normal for spatial reasoning. Pass centroid to create_sketch_on_face for chained feature creation.

Instructions

Listar caras — enumerate faces of one or all solid bodies.

Mirror of list_edges for faces. Returns one dict per face with: - index: 0-based per-body. NOT durable across rebuilds. - body_name: which body the face belongs to. - type: "planar" | "cylindrical" | "conical" | "spherical" | "other". Informational; addressing is by centroid. - centroid_mm: [x, y, z] in mm — midpoint of the face's bounding box. DURABLE reference for create_sketch_on_face. - area_mm2: face area in mm² (None if SW didn't expose it). - normal: outward normal [nx, ny, nz] for planar faces; None if SW didn't expose it. OMITTED on non-planar faces (token economy — structurally N/A there). - radius_mm, axis, concave: for cylindrical faces only — the geometry-side bore Ø used by verify_build_report; concave True = bore wall, False = outer boss/step face, None = unknown. OMITTED on non-cylindrical faces. - box_mm: axis-aligned bounding box [xmin,ymin,zmin,xmax,ymax, zmax] in mm (None if SW didn't expose it) — lets verify_build_report derive through-vs-blind from geometry (a bore face spanning both ends of the body is a through cut, regardless of the feature-definition read).

Args: body_name: If given, return only faces of that body. Else return faces of every solid body in the active part.

Use case: chained-feature LLM workflow. The LLM lists faces, reasons spatially ("the top face has the largest +Z normal"), passes the centroid to create_sketch_on_face, then sketches and extrudes/cuts on it.

Caveat: per-body face ordering is determined by SW's internal topology and is NOT durable across rebuilds. Re-run list_faces immediately before create_sketch_on_face rather than caching centroids across model edits.

Example — find the top face of a 50x50x20 block (sketched on Front, extruded +Z by 20): faces = list_faces() top = max( (f for f in faces if f["normal"] and f["normal"][2] > 0.9), key=lambda f: f["centroid_mm"][2], ) # top["centroid_mm"] = [25.0, 25.0, 20.0]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
body_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations exist, so the description fully handles transparency. It details non-durability of indices, field omissions based on face type, and explains when fields may be None. The example demonstrates expected output behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized into sections (summary, fields, args, use case, caveat, example). Every sentence adds value with no redundancy. It is detailed yet not overly verbose.

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 complexity of face geometry enumeration and the presence of an output schema, the description covers all needed aspects: return fields, parameter usage, durability, examples, and integration with other tools. It is fully self-contained.

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?

The single parameter 'body_name' is clearly explained: optional, filters to a specific body if given, otherwise returns faces from all bodies. This adds essential context beyond the schema's default value.

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 enumerates faces of solid bodies. It explicitly differentiates itself as a mirror of list_edges, and the verb 'list' accurately describes the action.

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

Usage Guidelines5/5

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

Explicit use case is provided (chained-feature LLM workflow) with an example. It also includes a caveat about non-durable indexes and advises against caching centroids across edits, guiding proper usage.

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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