get_health_series

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

With no annotations, the description carries the full behavioral burden and does well: it discloses that no thresholds or verdicts are applied, that empty days are simply absent, that failed fetches surface in `errors`, and that `at_wake` vs `most_recent` mean different things. It does not cover auth/permission requirements or explicit rate limits, leaving some gaps for a data-fetch tool.

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?

Front-loads the core purpose in the first sentence, then layers error semantics and field meanings in short, scannable paragraphs. It is longer than minimal, but nearly every sentence carries actionable information rather than filler.

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?

Despite no annotations, the definition covers purpose, all parameters, error/missing-data handling, and field interpretation, and an output schema exists so return values need not be fully re-explained. An agent has everything needed to select and invoke it correctly.

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%, so the description must compensate and it does: it documents date formats, inclusivity, the 60-day max on end_date, the full metric enum with a stated default, and the endpoint-sharing nuance between body_battery and stress. All three parameters gain meaning beyond the bare schema.

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?

States a specific verb and resource — 'Get raw multi-day health measurements in one call' — and pins down scope ('multi-day', 'no interpretation'), which distinguishes it from the per-domain siblings like get_sleep_data or get_body_battery. It never names an alternative explicitly, so the differentiation is inferred from scope rather than stated.

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?

Gives clear context: this is the raw input a coaching layer reasons over, and 'Fewer metrics means fewer Garmin requests' steers efficient usage. It also tells the agent how to interpret results (empty `errors` makes a short series trustworthy; don't read a gap as negative when `errors` is non-empty). It stops short of naming sibling tools as alternatives for single-metric queries.

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