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

Annotations already declare readOnly, idempotent, closed-world, so the safety profile is covered. The description adds genuinely useful output context: the result echoes inputs, emits data-quality warnings, and carries a required Labreador citation — all beyond what annotations provide.

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 tight sentences: the core action is front-loaded, the optional behavior follows immediately, and the return-value remark is packed efficiently. No 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?

With no output schema, the description correctly shoulders the burden by describing the response (echoed inputs, warnings, citation). Combined with 100% schema coverage on inputs, an agent has what it needs; only the model-choice rationale is unaddressed.

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%, so every parameter (model, signals, sampleSignals, concentrations) is already documented in the schema. The description reinforces that signals are OD and that sampleSignals drive interpolation, but adds no format or constraint detail beyond the schema. Baseline 3 is appropriate.

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: fitting a 4PL/5PL logistic curve to ELISA standards, plus the optional interpolation of sample concentrations from OD signals. The domain is fully unambiguous and trivially separable from the unrelated siblings (dietary_risk, pollution_indices).

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?

Usage is implied: fit a curve to standards and optionally interpolate samples. It says interpolation is optional but gives no guidance on when to prefer 4PL vs 5PL, nor any prerequisites for the input data. Adequate but leaves the model-selection decision to inference.

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