Advance the cloth physics for a specified number of steps to refine garment fit, with optional quality and particle distance adjustments.
Run the cloth simulation for N steps (about 30 steps per simulated second). quality: 0 normal, 1 stable, 2 accurate, 3 GPU-fast. particle_distance in mm (smaller = finer, slower; 20 default, 10 for final quality).
| Name | Required | Description | Default |
|---|---|---|---|
| steps | No | ||
| quality | No | ||
| particle_distance | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It adds useful behavioral context like 'about 30 steps per simulated second', quality tradeoff labels, and particle_distance effect on speed and fineness. But it does not disclose state mutation, reset requirements, or what the tool returns after running.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded. Every sentence provides concrete semantic or behavioral value, explaining parameters and performance without filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Parameter semantics are well covered, but the tool has no output schema and no annotations, so the description should also mention expected result, side effects, or prerequisites. It is adequate for choosing the tool, but incomplete for fully predicting behavior after invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description fully compensates. Steps, quality, and particle_distance are each given meaningful values and tradeoffs, including numerical scales and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear verb+resource: 'Run the cloth simulation for N steps.' It directly distinguishes this from siblings like simulation_reset or clo_stop_bridge by specifying the action and object.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is given, and no alternatives are named. It does not tell the agent whether simulation should follow garment construction or when to prefer simulation_reset or scene_export instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.