list_metrics

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

Annotations already mark this as readOnly and idempotent, so no safety contradiction exists. The description adds useful context about the listing including unit, day count, and date range, which goes beyond raw annotation data.

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?

Two sentences with no filler. The main action and return content are front-loaded, and the usage guidance is immediately actionable.

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?

For a zero-parameter discovery tool, the description fully covers what the agent needs: what will be listed, what fields are included, and when to use it. Sibling tool context supports the discovery-to-query flow, and nothing critical is missing.

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?

The tool has zero parameters, so parameter semantics are inherently simple. The description clarifies what the returned listing contains, which is useful given no output schema is present.

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 identifies a specific action (list), a clear resource (every available Apple Health metric), and the exact information included (unit, day count, date range). It clearly distinguishes this as a discovery tool from sibling querying 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?

The description explicitly states when to use this tool: 'discover metric names before querying.' This gives clear usage context and implies this is a prerequisite step rather than an analysis tool, though it does not explicitly name alternatives.

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