get_body_metric_history

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

No annotations are supplied, so the description carries the full disclosure burden. It says nothing about what the history looks like — sampling granularity, ordering, whether gaps are filled — nor that the default window is 180 days, leaving the agent to infer behavior entirely from the schema default.

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?

A single compact sentence, front-loaded with the core purpose and back-loaded with the example and prerequisite. No filler; the only cost is that the terseness borders on under-specification.

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?

An output schema exists, so return shape needn't be explained here. Still, for a 2-parameter tool with zero schema descriptions, the missing coverage of `days` and of history semantics (granularity, ordering) leaves the definition only marginally complete.

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 add real value for `types` by giving example values and a source for valid names. But the `days` parameter is never mentioned, so half the parameters remain undocumented in both schema and description.

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?

Names a specific resource (history of single body metrics) with a concrete example of the `types` values, which makes the operation recognizable. It implicitly contrasts with get_body_composition by pointing there for valid names, though it never explicitly says 'use this for time series, that one for a snapshot'.

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 pointer to get_body_composition gives the agent a prerequisite step (fetch valid metric names first), which is genuine routing guidance. However, there is no explicit statement of when to prefer this tool over siblings like get_body_composition or get_bio_age_history, and no exclusions.

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