list_body_metrics

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint false), the description adds behavioral context: it states BMI in the output is derived from the user's canonical height and that day's resolved weight, and instructs the agent not to recompute it. It also explains default parameter behavior (infer 30 days ago to today). These details help the agent understand side effects and derived data, exceeding annotation-only transparency.

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 concise and well-structured. It covers purpose, usage conditions, range constraints, and behavioral notes without unnecessary verbosity. Each sentence serves a distinct purpose: identifying the resource, stating when to use, noting the 31-day limit, and explaining parameter inference. No fluff or redundant information.

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 an output schema exists, the description need not explain return values. It fully covers input parameters, defaults, usage context, range limits, and derived data behavior. The description provides all necessary information for an agent to invoke the tool correctly in various scenarios, making it contextually 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?

The schema descriptions for start_date and end_date specify format (YYYY-MM-DD) and defaults ('30 days ago', 'today'). The description reinforces these defaults and adds the 'INFER — do not ask' guidance, making the parameter semantics fully clear. Schema coverage is 100%, and no enums exist, so the description effectively complements the 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?

The description clearly states the tool lists body composition entries within a date range. It specifies the resource ('body composition entries') and scope ('date range'), and the context signals show sibling tools include similar list tools, enabling an agent to distinguish this from list_workouts, list_meals, etc. The verb 'list' and explicit resource make the purpose unambiguous.

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 the tool ('when the user asks about their weight history, body fat trend, or any body metrics over time'). It also provides guidance for handling longer periods by making multiple calls with sequential date ranges, and instructs the agent to infer default parameters rather than asking the user. This gives clear, actionable usage instructions.

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