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Glama

Remote climate state and preset

toyota_get_climate
Read-onlyIdempotent

Check remote climate status: see if it is running, current temperatures, and the saved preset for target temperature, duration, defrosters, and heated seats.

Instructions

Remote pre-conditioning state (running or stopped, temperatures) and the saved preset a remote start would apply (target temperature, duration, defrosters, heated seats).

Use for: is the climate running? what temperature is the preset? Starting or stopping it is done with toyota_start_climate / toyota_stop_climate when remote commands are enabled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
is_onYes
presetYesWhat a remote start would apply.
statusYesRaw Toyota state, e.g. 'stopped', 'running'.
freshnessYes
started_atYes
duration_minutesNoProgrammed run time of the current session.
target_temperatureYes
current_temperatureYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare the tool as read-only and non-destructive, so the description does not need to restate that. It adds useful behavioral context by clarifying that starting/stopping is delegated to sibling tools and by enumerating what remote-start preset fields are returned. No contradiction with annotations.

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 compact and front-loaded with the tool's core purpose, followed by concrete usage questions and a pointer to sibling tools. Every sentence contributes meaningful guidance with no filler or repetition.

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

Completeness5/5

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

For a zero-parameter read-only tool with an output schema, the description covers what the tool returns, when to use it, and how it differs from related control tools. The agent has enough context to select and invoke it correctly without missing details.

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?

The tool has zero parameters and the input schema is already complete, so no parameter documentation is needed. The description still adds value by clarifying the conceptual scope of the request: it reads climate state and the saved preset rather than issuing a command.

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 the tool reports remote pre-conditioning state (running or stopped, temperatures) and the saved remote-start preset (target temperature, duration, defrosters, heated seats). It is explicitly distinguished from control tools by naming toyota_start_climate and toyota_stop_climate as the means to start or stop.

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

Usage Guidelines5/5

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

It gives concrete use cases: 'is the climate running? what temperature is the preset?' It also tells the agent that starting or stopping is not this tool's job, naming the exact sibling tools to use instead. This is explicit when-to-use and 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.