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Blender Copilot MCP Server

by dwgx

cloth_to_shape_key

Simulate cloth dynamics and capture the final draped state as a shape key for natural clothing rest poses.

Instructions

Run cloth simulation and save result as a shape key.

Useful for creating natural rest poses for skirts, capes, and other clothing that should hang naturally from a pin point.

Args: mesh_name: Name of the mesh object. shape_key_name: Name for the resulting shape key. pin_vertex_group: Vertex group for pinned vertices. frames: Simulation frames. stiffness: Cloth stiffness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
framesNo
mesh_nameYes
stiffnessNo
shape_key_nameNocloth_rest
pin_vertex_groupNo
Behavior3/5

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

With no annotations, the description must disclose behavioral traits. It mentions running a simulation and saving a shape key, implying a non-destructive addition. However, it lacks details on whether it modifies the mesh, requires an existing cloth modifier, or overwrites shape keys, and does not describe the return value or potential side effects.

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 concise, with two sentences in the main body followed by a clear list of arguments. No unnecessary words, and the purpose is front-loaded, making it easy for an AI agent to quickly understand the tool's function.

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 the tool's complexity (cloth simulation plus shape key creation) and the lack of output schema, the description provides high-level purpose and param descriptions but omits crucial details like prerequisites, error conditions, and return values. It is adequate but not fully complete for an agent to use without external knowledge.

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 input schema has 0% description coverage, so the description's Args section adds value by providing short explanations for all 5 parameters (e.g., 'Name of the mesh object', 'Simulation frames'). While terse, these descriptions clarify the purpose beyond the schema titles, compensating for the lack of schema descriptions.

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 runs a cloth simulation and saves the result as a shape key, with a specific use case for creating natural rest poses for clothing hanging from a pin point. It distinguishes from sibling tools like 'add_cloth' or 'sculpt_to_shape_key' by its unique combination of simulation and shape key conversion.

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?

The description implies usage for clothing that should hang naturally from a pin point, providing context for when to use it. However, it does not explicitly state when not to use it or name alternative tools, such as 'add_cloth' or 'physics_add_cloth', leaving some ambiguity for an AI agent.

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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