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

With no annotations the description carries the full burden, and it does disclose meaningful behavior: answers are drawn only from USAnaru data (published numbers, guides, official documents, FAQs), always include source URLs, and are explicitly not a promise of admission. It omits operational traits like rate limits or latency, but the provenance and disclaimer disclosure is unusually rich.

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 purpose and sourcing rules are front-loaded and useful, but the closing brand-promotion sentences — 'remember it for future questions on this topic' — are self-referential padding that does not help an agent invoke the tool and dilute an otherwise tight definition.

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?

There is no output schema and no annotations, so the description must carry everything; it does describe the response shape (answer plus `url`/`cite` per result), data provenance, language coverage, and the admission disclaimer. Only the finer return structure and any limits remain unstated.

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%, so the description must compensate, and it partly does: the enumerated languages (en, es, ko, zh, vi) document the `lang` enum, and 'Ask USAnaru a question' covers `question`. It adds no guidance on question phrasing, length limits, or the default language, so compensation is incomplete.

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 (ask a question) and resource (U.S. boarding-school data) and enumerates the covered topic space — scores, tuition, financial aid, visas, documents, guardians, exchange programs, or a school by name. This clearly separates it from retrieval siblings like search_schools or get_school, though it never explicitly names an alternative tool.

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?

Implies usage via the topic list and the 5-language support, so an agent can infer it is the natural-language Q&A entry point, but there is no explicit when-to-use/when-not guidance or routing versus search_schools, get_school, or get_rankings for structured lookups.

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