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create_lop_node

Create a LOP node under a specified parent path in Houdini, using a verified node type and optional name and USD prim path.

Instructions

Create a new LOP node.

Before using this, call list_node_types(context='Lop', filter='') to verify the correct node type. Solaris ships many specialized LOPs — sublayer, reference, materiallibrary, assignmaterial, karmarendersettings, editproperties, xform, prune, configurelayer, collection, addvariant — that may not be obvious from their names.

Args: parent_path: Parent node path. lop_type: LOP node type (e.g. "sphere", "sublayer", "merge"). name: Node name. prim_path: USD prim path to set on the node.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
lop_typeYes
prim_pathNo
parent_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.24.1
    • addedInput schema / additionalProperties
      Added value: +false
  2. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries full behavioral burden. It discloses the prerequisite validation step and that prim_path sets a USD prim path on the node, but omits failure modes, undo behavior, name-collision handling, and what the operation returns. The added context is useful but incomplete for a mutation tool.

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-loads the core action, then provides a prerequisite and an args list. The list of twelve example LOP types is somewhat verbose but illustrative. Overall the structure is clean and every part serves a purpose, though the example list could be trimmed.

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 description covers purpose, a prerequisite, and brief parameter hints. But for a create tool with no annotations, no output schema, and 0% schema description coverage, it should also describe the return value, side effects, and error conditions. Those gaps leave an agent under-informed about invocation outcomes.

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?

Schema coverage is 0%, so the description must explain all four parameters. It gives meaningful detail for lop_type (with examples and a validation step) and prim_path (explains its effect), but parent_path and name are essentially tautological restatements. Format expectations, defaults, and requiredness remain unstated.

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?

States a specific verb and resource: 'Create a new LOP node.' This distinguishes it from generic siblings like create_node and from context-specific creators like create_light. However, it does not explicitly name or contrast with alternative node-creation tools.

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?

Gives a clear precondition: 'Before using this, call list_node_types(context=\'Lop\', filter=\'<keyword>\') to verify the correct node type.' It also explains why this matters by listing many specialized LOPs. No explicit when-not or direct sibling comparison is offered.

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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