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bevel_edges

Destructive

Set width and segments to round or chamfer edges in a Blender model, targeting edges sharper than an angle or inside a specified region.

Instructions

Round or chamfer the edges sharper than angle degrees (optionally only those whose midpoint is inside where, which uses x, y, z ranges and box). width is in metres (empty: 0.02 m, less on objects under 0.3 m), segments 1 is a chamfer, 3 is round. profile 0.5 is circular. width_type width is the distance between the two new edges. Only edges with two faces can be bevelled. Overlapping bevels are clamped, and one tight edge limits every bevel of the call: the answer gives width (asked), achieved_width (min and median measured on the result, in metres), clamped (edges under 90% of the asked width) and a warning when most are clamped. Doubled vertices and zero-area faces are removed afterwards (merged_vertices, removed_zero_faces).

Input Schema

NameRequiredDescriptionDefault
angleNo
whereNo
widthNo
objectYes
profileNo
segmentsNo
width_typeNooffset

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4/5.0
Behavior5/5

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

Despite annotations already flagging destructiveHint=true, the description adds substantial behavioral context: clamping of overlapping bevels, the 'one tight edge limits every bevel' rule, the returned fields (achieved_width, clamped, warning), and post-processing that removes doubled vertices and zero-area faces. This goes well beyond the annotation layer.

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 purpose is front-loaded and every sentence carries technical information (defaults, return fields, cleanup). It is dense and runs long as a single block, but there is little wasted wording.

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?

For a 7-parameter mesh-mutation tool with no output schema, the description supplies return-value semantics and clamping/warning behavior, which is exactly what an agent needs. Coverage of the remaining width_type enum values is the only notable omission.

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?

With 0% schema description coverage, the description carries the burden and does so well: it defines width units and default, segments (1=chamfer, 3=round), profile (0.5=circular), width_type 'width' semantics, and where as x/y/z ranges and box. It omits the other enum values (offset, depth, percent, absolute), leaving a small gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening clause names a specific verb and resource: 'Round or chamfer the edges sharper than `angle` degrees', which uniquely identifies a bevel operation. It does not name any sibling (e.g. weld, subdivide, extrude_faces) to disambiguate, so it is clear but stops short of explicit sibling differentiation.

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

Usage Guidelines3/5

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

Usage conditions are implied through constraints ('Only edges with two faces can be bevelled', angle threshold, optional `where` filter) rather than stated as when-to-use guidance. There is no explicit statement of when to prefer this over alternatives like subdivide or inset_faces.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.