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create_light_rig

Set up professional lighting rigs in Blender for 3D scenes. Choose from preset configurations like three-point, studio, rim, or outdoor lighting to enhance rendering quality.

Instructions

Create a pre-built lighting rig (multiple lights arranged for common setups).

Args: type: Rig type. One of: THREE_POINT, STUDIO, RIM, OUTDOOR. target: Optional name of the object the rig should point at. intensity: Overall intensity of the lights, default 1000.

Returns: Confirmation dict with names of all created lights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes
targetNo
intensityNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool creates lights but doesn't disclose behavioral aspects like whether it requires specific permissions, if it modifies existing scenes, what happens on failure, or any rate limits. The return format is mentioned but without details on error handling or 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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value, though the 'Returns' section could be slightly more detailed given the lack of output schema description.

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?

For a creation tool with no annotations and an output schema, the description covers purpose and parameters adequately but lacks behavioral context and usage guidance. The output schema existence reduces the need to explain return values, but more operational details would improve completeness.

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?

With 0% schema description coverage, the description compensates well by explaining all three parameters: 'type' with enum values, 'target' as optional object name, and 'intensity' with default. It adds meaningful context beyond the bare schema, though it doesn't detail parameter interactions or constraints.

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 creates a pre-built lighting rig with multiple lights arranged for common setups. It specifies the verb 'create' and resource 'lighting rig', distinguishing it from sibling tools like 'create_light' (single light) or 'create_object' (general object).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'create_light' or other lighting-related tools. It mentions common setups but doesn't specify scenarios or prerequisites for choosing this over manual light creation.

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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