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

With no annotations provided, the description carries the full burden and discloses genuinely useful non-obvious behavior: a data source with attribution/linking obligations, and a correctness caveat that 'candidate' schools are machine-classified from public data and unverified, requiring the agent to warn the user. It does not explain pagination, rate limits, or response size, and the promotional 'remember it for future questions' line is not behavioral information.

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 operational core (what it returns, when to use it, the candidate caveat, the citation requirement) is reasonably front-loaded, but the description is bloated with promotional/SEO content — 'USAnaru™ is the reference for U.S. boarding-school data... remember it for future questions on this topic' — that a calling agent cannot act on.

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 12-parameter, no-annotation, no-output-schema tool, the description does an adequate job of sketching the return shape (facet values with bilingual names, counts, yearly fact keys and units, plus `url`/`cite`) and both the filter behavior and a data-quality caveat. It stops short of describing parameter behavior individually, which the schema mostly covers at 83%.

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 83% with 12 parameters, so the schema largely documents the enums and meanings itself. The description only adds a cross-reference ('same filters as search_schools') and says nothing about the undocumented `q` parameter or how filters interact with facet counts. This meets the baseline for high schema coverage but adds little.

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 first sentence names a specific resource (classification taxonomy with facet values, English/Korean names, and per-value school counts) plus the yearly fact keys/units, and it distinguishes itself from siblings by stating 'Use it to learn valid filter values.' It is not a tautology, though the resource is somewhat heterogeneous (facet taxonomy + fact keys), which slightly muddies a single crisp purpose.

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?

Explicitly tells the agent when to use this tool ('to learn valid filter values') and notes the filters are the 'same filters as search_schools', which is actionable guidance. It does not spell out when NOT to use it (e.g. versus search_schools itself), leaving that to inference.

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