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light_set

Modify an existing light's position, rotation, look-at target, power, color, and on/off state to control scene illumination.

Instructions

Changes a light: placement, aim, power, color, on/off.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "light": "<light>"
    },
    "name": "light_set"
  }
}

Input Schema

NameRequiredDescriptionDefault
onNoSwitch the light on or off.
colorNo
labelNoNew label.
lightYesLight label.
powerNo
look_atNo
positionNo
rotationNo
propertiesNo
spread_angleNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds no behavior beyond that: with ten optional parameters it never says whether omitted fields are left untouched or reset, whether the call fails on an unknown light label, or what happens to units. The field list largely restates the schema.

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?

It is a single front-loaded sentence with zero filler. The field enumeration is compact, though the partial list of aspects reads as incompleteness rather than deliberate scoping.

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

Completeness2/5

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

For a 10-parameter mutation tool with no output schema and no documented return value, the definition is thin. The key omission is patch semantics for the nine optional parameters, plus what identifies or validates the required light label; without that, an agent cannot confidently predict the effect of a partial call.

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 only 30% for ten parameters, so the description needs to compensate, and it does not. It maps roughly five of the parameters (position, look_at, power, color, on) to plain words while ignoring label, rotation, properties, and spread_angle, and it adds no syntax, units, or range detail the schema does not already give (for example, the cm units come from the schema, not the description).

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 names a concrete verb and resource ("Changes a light") and enumerates the modifiable aspects: placement, aim, power, color, on/off. That is enough to distinguish it from light_create, which the agent would infer is for making new lights, but it does not explicitly contrast the sibling.

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?

Usage is only implied. The verb "changes" suggests this applies to lights that already exist, but the description never states prerequisites (does the light have to exist?), nor does it name any alternative tool or exclusion. Nothing actively misleading, but the agent must infer the context.

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