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SekaiNoOwari77

mcp-3d-modeling-agent

blender_mesh_proportional_transform

Transform selected mesh vertices with proportional falloff to achieve organic deformations and smooth shape refinement.

Instructions

Move, rotate, or scale vertices with proportional falloff affecting neighbors — organic shape refinement, smooth deformations, sculpt-like adjustments via MCP.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesTransform value: [x,y,z] for translate/scale, [angle_degrees, axis_x, axis_y, axis_z] for rotate
radiusNoProportional influence radius
falloffNoFalloff curve typeSMOOTH
object_nameYesName of the mesh object
transform_typeNoTransform typeTRANSLATE
vertex_indicesYesCenter vertex indices for the transform

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.4.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does explain the central side effect—proportional falloff affects neighboring vertices—and lists the supported transform modes. However, it omits operational details such as whether the object must be in edit mode, whether the operation is undoable, or how mesh data is committed.

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 a single, front-loaded sentence that communicates the core action and use cases efficiently. The trailing 'via MCP' adds little value, but it is minor and does not detract significantly from the overall clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given six parameters, no annotations, and no output schema, the description adequately conveys when to use the tool but leaves gaps around prerequisites like edit mode, object type expectations, and failure behavior. It is minimum viable but not fully self-sufficient for correct invocation without further inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers all six parameters with 100% description coverage, including enum options and defaults. The tool description adds context about proportional falloff behavior but does not add new parameter-level semantics beyond the schema, so the baseline score of 3 is appropriate.

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 states specific verbs and a specific resource: move, rotate, or scale vertices, with proportional falloff affecting neighbors. This clearly distinguishes the tool from object-level transforms like blender_object_transform and from other mesh editing operations. The intent is immediately recognizable.

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 supplies clear contexts for use: organic shape refinement, smooth deformations, and sculpt-like adjustments. It does not explicitly state when not to use it or name alternatives such as blender_object_transform, but the intended scenarios are specific enough to guide selection.

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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