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

No annotations are provided, so the description carries the full burden. It does disclose that the result includes an observed-CL band and applicability warnings, and warns when results rest on predicted inputs, which is real behavioral context. It omits permissions, units, failure behavior, and how the warnings are surfaced.

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 dense sentences with no filler; the core computation is front-loaded and the conditional instruction follows. Slightly telegraphic, but 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?

For a 9-parameter, no-annotation, no-output-schema domain tool, the description conveys the computation, the key returned artifacts (band, warnings), and a cross-tool caveat. It is largely sufficient, though it could say more about output units and structure since no 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 100% across 9 parameters, with rich per-field documentation (units, ranges, model semantics, clint_censored behavior). The description adds only a provenance caveat for fu_p/fu_mic, so the schema does the heavy lifting and the baseline of 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?

The description names a specific computation (hepatic and plasma clearance via IVIVE) and its inputs (CLint, fu,mic, fu,p), plus what comes back (observed-CL band, applicability warnings). It does not distinguish this tool from siblings like scale_to_human or target_clint, so it stops short of a 5.

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 by the IVIVE framing, and there is one conditional instruction about fu,p/fu,mic provenance from predict_inputs. However, there is no explicit guidance on when to choose this tool over scale_to_human or target_clint, nor any prerequisites or exclusions.

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