get_metric_definitions

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

With no annotations, the description carries full responsibility and succeeds. It discloses that no account data is fetched, no training calculation is performed, unknown selectors appear in unknown_names, Tymewear volume is relative rather than calibrated liters, and the catalogue does not identify the recording device.

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

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

The description is lengthy but information-dense; each sentence contributes usage guidance, behavioral disclosure, or a necessary caveat. The core purpose is front-loaded, followed by when-to-use, then important limitations.

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

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

Given that an output schema exists, the description does not need to enumerate return fields. It covers selector optionality, default catalogue behavior, unknown-name handling, unit caveats, and device limitations, making it complete for a catalogue-lookup tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates well. 'Selectors are optional; omitting both returns the small curated catalogue' explains the default behavior of both parameters, and 'unknown selectors remain explicit in unknown_names' clarifies failure semantics. Concrete metric examples like VT/tidal_volume add practical meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Explain selected metric names and fields from the local catalogue.' It further distinguishes itself from data-fetching and computation tools by stating 'No account data is fetched, no training calculation is performed,' making its role clear among the sibling tools.

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

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

It gives explicit timing guidance: 'Use this before interpreting activity streams, intervals, wellness, or custom-item content.' It does not name specific alternative tools or state when not to use it, but the usage context is clear enough for an agent to decide.

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