add_hydration_data

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 so exceptionally well: it discloses accumulation semantics, negative-value subtraction, the zero floor, the exact API error message on further subtraction, and the fact that the Garmin API offers no delete so zeroing is the closest to removal. These are exactly the traits an agent needs before mutating hydration 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?

The most decision-relevant facts (accumulation, negative correction, zero floor) are front-loaded in the first two paragraphs, and the Args block is a compact reference. Length is justified by genuinely non-obvious behavior rather than padding.

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 3-param mutation tool with an output schema, the description covers everything the agent needs: mutation semantics, correction workflow, floor behavior, error text, and the absence of a delete path. Return values are legitimately omitted since an output schema exists.

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 the Args block does: value_in_ml is 'amount of liquid in millilitres' with negative-subtracts semantics, date is YYYY-MM-DD, and timestamp is YYYY-MM-DDThh:mm:ss.sss. It even explains why the argument is named `date` rather than the historical `cdate`.

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 opening sentence gives a specific verb and resource ('Add a hydration entry for a date'), so the agent immediately knows this is a write operation on daily hydration. It references the sibling get_hydration_data only incidentally (for the date argument naming), not as a routing cue, so the sibling distinction is present but not fully exploited.

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 effectively tells the agent when and how to use this tool: entries accumulate, and a negative value_in_ml is the documented way to correct an over-log. It does not, however, explicitly say to read with get_hydration_data first or name any exclusions, so it stops short of full when/when-not guidance.

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