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list_edges

Enumerate edges of solid bodies and obtain midpoint coordinates for reliable fillet and chamfer operations.

Instructions

Listar aristas — enumerate edges of one or all solid bodies.

Returns one dict per edge with: - index: 0-based per-body. NOT durable across rebuilds. - body_name: which body the edge belongs to. - type: "line" | "circle" | "other" - midpoint_mm: [x, y, z] in mm. DURABLE reference for fillet/chamfer. None for closed-loop edges (full circles) — a circle has no canonical midpoint. For those, pass edge_indices to fillet/chamfer instead of edge_midpoints_mm. Partial arcs (post-fillet corner arcs) and line edges DO have midpoints. - length_mm: edge length in mm (None for closed loops).

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

Use case: pre-fillet/pre-chamfer LLM workflow. The LLM enumerates edges, reasons spatially ("the four top edges have z=10mm"), then passes midpoints to fillet() / chamfer().

Caveat: in this SolidWorks binding, edges can't be selected by name string in part-doc context — coordinate matching is the only durable address. Use the midpoint values returned here verbatim; don't recompute them in the LLM.

Example — list every edge in the active part: edges = list_edges() # edges = [{"index": 0, "body_name": "Saliente-Extruir1", # "type": "line", "midpoint_mm": [0, 0, 5], ...}, ...]

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?

With no annotations provided, the description fully discloses behavioral traits: return structure, durability of indices, midpoint behavior (None for closed loops), and the caveat about coordinate matching. This exceeds what annotations would typically cover.

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 somewhat lengthy but well-structured with clear sections for return fields, args, use case, caveat, and example. It is front-loaded with the purpose, and each sentence adds value, though some fat could be trimmed.

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's simplicity (one optional parameter, output schema exists), the description fully covers the return structure, durability, usage context, and example. It is complete for an agent to use effectively.

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?

The single optional parameter 'body_name' is well explained: if given, return edges of only that body; otherwise, return edges of all solid bodies. This adds meaning beyond the schema's type definition.

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 edges of solid bodies, with a specific verb ('Listar' and 'enumerate') and resource ('edges of one or all solid bodies'). It distinguishes itself from sibling tools like list_faces or list_dimensions by focusing exclusively on edges.

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 explicitly describes the use case as a pre-fillet/pre-chamfer LLM workflow, providing clear context. It does not explicitly state when not to use it or compare to alternatives, but the context is sufficient for the agent to infer appropriate 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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