Skip to main content
Glama

create_light_rig

Generate pre-built multi-light setups in Blender for three-point, studio, rim, or outdoor scenes, with optional target and intensity control.

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.7.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / type / enum
      Added value: +[
      +  "OUTDOOR",
      +  "RIM",
      +  "STUDIO",
      +  "THREE_POINT"
      +]
  2. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that the operation creates multiple lights and returns their names, but says nothing about whether created lights are parented/grouped, whether an existing rig is replaced or duplicated, permissions, or reversibility (delete_light exists as the inverse).

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-loaded one-line purpose followed by documented args and returns; the docstring structure is clean and every line contributes. Slightly verbose as a Google-style docstring for such a simple tool, but no wasted prose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-param creation tool with an output schema present, the description covers purpose, all param semantics, and the return shape, so the agent has what it needs to invoke correctly. It stops short only on behavioral edges (grouping, idempotency, replacement of existing rigs).

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?

Schema description coverage is 0%, so the description must compensate, and it does well: it names the enum options (THREE_POINT, STUDIO, RIM, OUTDOOR), explains target as the object the rig points at, and gives the intensity default (1000). Minor gap: it doesn't state intensity units.

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?

States a specific verb+resource ('Create a pre-built lighting rig') and even parenthetically clarifies it makes multiple lights arranged for preset setups, which distinguishes it from the sibling create_light. An agent can immediately tell this composes a rig rather than a single light.

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?

No when/when-not guidance and no mention of alternative siblings (create_light, set_light_property, list_lights). The description never says when to prefer a pre-built rig over manually placing individual lights, so an agent must infer the use case.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools