probability.f_ppf

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

Annotations already declare readOnly/idempotent/closed-world, and the description adds value beyond them: computation via scipy float64 and a result rounded to 10 significant digits returned as a string. It also states the input domain (0<q<1, positive df), though it doesn't say what happens on out-of-domain input.

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?

Three sentences, no filler: purpose first, then parameter/return semantics, then the practical usage tip. Given the zero schema coverage, none of it is redundant.

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?

Although an output schema exists, the description still pins down the return form (10-significant-digit string) and the numeric engine, plus full parameter semantics and a usage hint. Nothing needed to invoke it correctly is missing.

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% — properties carry only bare titles ('Q', 'Dfn', 'Dfd'). The description fully compensates: q is the probability in (0,1), dfn the numerator df, dfd the denominator df, and all are positive decimal strings. Every parameter gets meaning the schema does not provide.

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+resource: 'F 분포의 분위수를 구한다' (computes the quantile of the F distribution). Quantile/percent-point is unambiguously distinct from the f_cdf and f_pdf siblings, so an agent can route correctly without opening schemas.

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?

Gives a concrete usage rule: for the critical value at significance level α, pass 1-α as q. That is a real when-to-use cue. It stops short of naming an alternative (e.g. use f_cdf for the cumulative probability) or stating when not to use it.

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