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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds substantial behavioral context beyond that: token-length-dependent search behavior (≥3 chars FTS vs 1-2 chars LIKE), the distinction between `source` and `org`, the raw HTML nature of `rel_stds`, and the legacy-source date caveat. No contradiction with annotations.

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 description is long but tightly organized into [Purpose], [Usage], [Response], and [Rules] sections. Every sentence earns its place, and the most important selection criterion is front-loaded. There is no filler or repetition.

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?

With no output schema, the description fully defines the response shape and field semantics, and it explains how to handle `rel_stds` HTML, how to use `qna_key` with get_qna, and how legacy data should be treated. This is sufficient for correct invocation and post-processing.

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?

Although the schema covers both parameters, the description adds real semantic value: it explains query tokenization rules (FTS vs LIKE), shows concrete example queries, and demonstrates how `limit` is used with 'limit=30'. This is meaningful guidance an agent would not get from the schema alone.

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 opens by stating the tool name, the action ('전문검색'), and the exact resource ('3,669건 질의회신'). It also distinguishes itself from get_paragraph's more precise related_qnas path for paragraph-linked answers, making correct selection straightforward.

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 [Purpose] section explicitly explains when to use search_qnas: for official 회신 that the 기준서 text alone cannot settle. It also provides an explicit when-not: for 회신 tied to a 문단, get_paragraph's related_qnas is more precise. The [Usage] examples reinforce with concrete query strings and a limit setting.

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