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

Annotations already establish this as a safe, closed-world, side-effect-free read, so the description doesn't need to re-state safety. It adds that a risk stratum is produced alongside the Grade Group, but it does not disclose the mapping logic (e.g. how 3+4 vs 4+3 are grouped) or how ambiguous inputs such as grades outside 1–5 are handled — relevant for a scoring tool whose whole value is the rule it applies.

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 pipe-delimited USE WHEN / ALIASES / RETURNS layout is front-loaded and scans quickly, with zero filler sentences. The bilingual alias list is long but serves recall for Chinese-language queries, which is a legitimate use of space.

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 deterministic two-parameter calculator with no output schema, the description states the inputs and names both return values (Grade Group and risk stratum), which is enough to call it correctly. Only the grouping rules and input validation behaviour are left implicit, a minor gap given the tool's simplicity.

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 coverage is 100%: both parameters are documented as 1–5 primary and secondary grades, so the schema carries the semantics. The description contributes nothing further about the parameters (no note on ordering, defaults, or whether a single composite score is accepted), which is the baseline case rather than an added-value case.

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 'USE WHEN' clause names the exact transformation: Gleason score in, Grade Group plus risk stratum out, scoped to prostate cancer. That is a specific input→output operation with a concrete domain, and it is unmistakably distinct from neighbouring pathology/statistics tools such as fibrosis_staging or ihc_hscore.

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 trigger condition is stated explicitly ('USE WHEN: Gleason score → Grade Group'), so an agent knows exactly which query routes here. It stops short of exclusions or naming an alternative tool (e.g. for whole-pathology staging), which is the only gap.

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