withings_get_body_composition

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

With no annotations, the description carries the full behavioral burden. It discloses that this is a read ('Fetch') and specifies which measure types are involved, but omits pagination semantics (the offset parameter references a previous 'more=true' response) and the response shape. No annotation contradiction.

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 short sentences with zero filler: verb and resource up front, then the metric list with type codes, then the source context. Every clause earns its place.

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?

Adequate for a straightforward fetch — it names the metrics and source. But with no output schema and no annotations, the agent is left without pagination flow (how to use offset with the 'more=true' flag) or response structure; the schema covers defaults, yet the overall picture has clear gaps.

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 100% — offset, enddate, and startdate are all documented with types, units, and defaults. The description adds no parameter-level meaning, so the baseline 3 applies.

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 ('Fetch scale body composition') and enumerates the exact metrics (fat free mass, fat ratio, fat mass, muscle, hydration, bone) plus their Withings type codes (5,6,8,76,77,88). The metric list and type codes distinguish it from siblings like withings_get_weight and withings_get_measurements.

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?

'Typical source: Withings scale' implies the tool is for scale-derived body composition data, giving a loose selection cue. However, it never names alternatives, states when-not-to-use, or provides explicit routing among the 16 siblings.

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