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blend-ai

by HoldMyBeer-gg
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Python
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set_edge_crease

Adjust edge crease values on a mesh to control how sharp edges stay when applying a Subdivision Surface modifier. Choose all edges or only current selection.

Instructions

Set edge crease value on all edges for subdivision surface control.

Crease values control how sharp edges remain when a Subdivision Surface modifier is applied. 0 = fully smooth, 1 = fully sharp.

Args: object_name: Name of the mesh object. value: Crease value. Range: -1.0 to 1.0.

selection: "ALL" to act on the whole mesh, or "CURRENT" to act only
    on what is already selected. Use the select_* tools to choose
    first. Defaults to "ALL".

Returns: Confirmation dict.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueNo
selectionNoALL
object_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.7.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / selection
      Added value: +{
+  "default": "ALL",
+  "enum": [
+    "ALL",
+    "CURRENT"
+  ],
+  "title": "Selection",
+  "type": "string"
+}
  2. Addedv1.2.2

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does explain the domain effect of crease values and confirms a dict return, but it omits operationally relevant traits: whether edit mode is required, whether existing creases are overwritten, and whether the change is undoable.

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?

Front-loaded with the purpose and mechanism, then a clean Args/Returns block. The value-semantics sentence earns its place, though the blank-line padding and the redundant 'Confirmation dict' note add some length without adding much.

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 3-parameter mesh-editing tool with an output schema, the description supplies the conceptual meaning, parameter ranges, and selection modes an agent needs. What remains missing is workflow context: modal/edit-mode prerequisites and how this interacts with other edge-attribute tools like mark_sharp.

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 must compensate, and it largely does: value range (-1.0 to 1.0), selection enum meanings and default, and object_name all documented. Minor gap: negative values are inside the stated range but their effect is never explained, so the semantics of the lower half of the range are undefined.

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?

States a specific verb (set) and resource (edge crease) plus the mechanism it controls (subdivision surface sharpness). The qualifying sentence about subdivision surface modifiers distinguishes it from adjacent tools like mark_sharp or bevel_edges without naming them directly, which is enough for an agent to differentiate.

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?

Explains the ALL vs CURRENT semantics and tells the agent to use the select_* tools first when targeting a subset, which is real routing guidance. It stops short of naming an alternative tool (e.g. mark_sharp) or stating when-not to use this, so it is clear context rather than full exclusion guidance.

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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