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set_shadow_settings

Configure shadow settings for a Blender light object by enabling or disabling shadows and setting the soft shadow radius.

Instructions

Configure shadow settings for a light.

Args: object_name: Name of the light object. use_shadow: Whether to enable shadows, default True. shadow_soft_size: Soft shadow radius, default 0.25.

Returns: Confirmation dict.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
use_shadowNo
object_nameYes
shadow_soft_sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.7.0
    • removedInput schema / properties / name
      Removed value: -{
      -  "title": "Name",
      -  "type": "string"
      -}
    • addedInput schema / properties / object_name
      Added value: +{
      +  "title": "Object Name",
      +  "type": "string"
      +}
    • changedInput schema / required
      Previous value: -[
      -  "name"
      -]New value: +[
      +  "object_name"
      +]
  2. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden, and it mostly does not. It states parameter defaults but omits whether the object must exist, whether the object_name must be a light specifically, idempotency, or permission requirements. The 'Returns: Confirmation dict' line is largely redundant given an output schema exists.

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 purpose sentence is front-loaded and the Args block maps cleanly to the schema. It is compact with no filler, though the Returns line adds little given the output schema.

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?

An output schema exists, so the return value needn't be spelled out, and all parameters are covered. But for an unannotated mutation tool, the description lacks behavioral context such as whether the target light must exist or what happens toggling shadows off, leaving it only minimally viable.

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 document all three parameters with intent and defaults (use_shadow default True, shadow_soft_size default 0.25). The only gap is that the unit/range of shadow_soft_size is unspecified.

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 description states a specific verb and resource: 'Configure shadow settings for a light.' This tells the agent exactly what the tool manipulates. However, it does not distinguish itself from the sibling set_light_property, which likely overlaps, leaving the agent to guess which light-configuration tool to pick.

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?

There is no guidance on when to use this tool versus set_light_property or create_light, no prerequisites (e.g. the light must already exist), and no exclusions. The agent must infer applicability entirely from the name and purpose sentence.

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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