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butterflyskies

elgato-keylight

apply_preset

Apply a lighting preset like bright, dim, warm, cool, or video to selected Elgato Key Lights. Adjust multiple lights to a consistent setting with one command.

Instructions

Apply a named lighting preset.

Available presets: bright, dim, warm, cool, video.

Args: preset_name: Name of the preset to apply. light_names: Optional list of light names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
light_namesNo
preset_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description must fully disclose behavioral traits. It mentions applying a preset but does not specify if it overrides all lights, whether it resets unsaid settings, or any side effects. The mutation nature is clear, but missing details like immediate effect or reversibility.

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 concise with two short sentences plus a list of presets and Args section. Every part adds value, and the structure is front-loaded with the core action. Could be slightly more compact by merging lists, but overall efficient.

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 simple tool with an output schema (context indicates it exists), the description adequately defines the action and parameters. However, it lacks details about what happens upon apply (e.g., all lights change, effect duration) and does not reference the output. Acceptable but not robust.

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 description must compensate. It explains preset_name as 'Name of the preset to apply' and light_names as 'Optional list of light names', which adds some meaning beyond the schema (which only defines types). However, it could be more descriptive, e.g., list of allowed presets or format.

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 clearly states it applies a lighting preset, lists available presets (bright, dim, warm, cool, video), which distinguishes it from sibling tools that set individual attributes like brightness or temperature. The verb 'apply' combined with 'named lighting preset' is specific and contextual.

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 explicit guidance on when to use this tool versus alternatives like set_brightness or set_mood_lighting. It does not mention prerequisites, preferred scenarios, or situations where other tools are better suited. The usage context is only implied.

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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