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HueMCP

HueMCP

by mbruton
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

activate_scene

Recall a saved scene to apply its light settings to the target group, with optional brightness and transition overrides.

Instructions

Recall a scene — applies its stored light states to its target group.

  • action is active (default), dynamic_palette (cycles through the scene's palette), or static (one-shot apply with no dynamics).

  • brightness (0–100) overrides the scene's stored brightness.

  • transition_ms overrides the scene's default fade duration.

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": {
      "scene_id": "<scene_id>"
    },
    "name": "activate_scene"
  }
}

Input Schema

NameRequiredDescriptionDefault
actionNoactive
scene_idYes
brightnessNo
transition_msNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the key behaviors (applying states, overrides, action types) but does not mention error handling, side effects, or prerequisites like scene existence.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear purpose followed by a bulleted list of parameters. Every sentence adds value with no redundancy.

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?

Given the complexity (4 params, no annotations, output schema present), the description covers main behavior and parameter meanings. It lacks details on error handling or invalid inputs but is generally sufficient.

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 coverage is 0%, so the description adds essential meaning: action values, brightness range (0–100), transition_ms override. It compensates well for the lack of parameter descriptions in the schema.

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 'Recall a scene — applies its stored light states to its target group' with a specific verb and resource. It distinguishes from sibling tools like list_scenes, set_light, etc.

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

Usage Guidelines4/5

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

The description explains the action parameter options and brightness/transition overrides, providing clear context for use. However, it lacks explicit exclusions or when-not-to-use guidance.

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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