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ttiimmaacc

mcp-for-clo3d

by ttiimmaacc

apply_fabric_json

Apply a .jfab JSON file to a fabric, merging physical properties and resetting omitted keys to CLO3D defaults.

Instructions

Apply a .jfab file (CLO's JSON fabric format, see JFABSpec.json in the CLO SDK) to a fabric. Keys the file leaves out at the top level keep their values (colour, texture, content), but a "mapPhysical" block replaces the whole physical set: its missing keys reset to CLO's defaults, and a missing "qsFabricName" clears the name. set_fabric_physics handles this for you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
fabric_indexYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A3.6/5.0
Behavior5/5

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

With no annotations, the description carries the full disclosure burden, and it does so well. It explicitly explains that top-level omitted keys keep their values, that mapPhysical replaces the entire physical set, that missing physical keys reset to defaults, and that a missing qsFabricName clears the name. This is unusually transparent about potentially destructive 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main action and then packs important behavioral details into the second sentence. It is not overly long, and nearly every sentence adds value. The final sentence about set_fabric_physics is somewhat ambiguous but not wasteful.

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?

The core merge/reset semantics are well covered, which is the most important part for avoiding incorrect calls. However, there is no output schema, no mention of return values, error behavior, prerequisites, or how fabric_index should be obtained. An agent still has to infer some operational details before invoking the tool safely.

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

Parameters2/5

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

Schema description coverage is 0%, and the description never actually names file_path or fabric_index. It implies that the file is the .jfab and the fabric is the target, but it does not explain whether fabric_index is zero-based, what path format is expected, or how the target fabric is identified. The description does too little to compensate for the bare schema.

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 first sentence clearly states a specific action and resource: apply a .jfab file to a fabric. It also references the CLO SDK spec, which adds precision. However, it does not explicitly contrast itself with siblings like set_fabric_physics or set_fabric_information, and the closing reference to set_fabric_physics is slightly ambiguous.

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 gives helpful context about applying a .jfab file and mentions set_fabric_physics handles part of the process. But it does not explicitly state when to prefer this tool over alternatives, when not to use it, or what prerequisites exist. Usage guidance is implied rather than direct.

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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