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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals absence semantics (relevance=low / lacks_answer=true), requires honest reporting that den does not hold the material, details scope behavior (scope_split, scope_partial, scope_absent), and states that out-of-scope data are disclosed rather than discarded. These are real behavioral traits an agent could not infer from the schema.

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 well structured: purpose is front-loaded, followed by bulleted call conditions, return behavior, the anti-fabrication rule, and as_of usage. Every sentence carries operational value, and the bold and bullet formatting makes it easy for an agent to scan.

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?

Given the presence of a full input schema and an output schema, the description fills the remaining contextual gaps: query domain, sourcing expectations, no-data handling, scope-disclosure behavior, and past-date queries. There is no critical operational missing piece for correct selection or invocation.

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%, so the baseline is 3. The description meaningfully enriches the question parameter with example query categories (numerical standards, clauses, construction sequences, design requirements) and reinforces as_of usage for past 기준. However, it largely leaves limit and profile semantics to the already-detailed schema, so the added value is real but not maximal.

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 states a specific verb and resource: it finds original numeric values and clause text in Korean construction standards (KDS/KCS/KS) and building statutes. Concrete example queries and the explicit return type (original text plus source citation) make the tool’s role unmistakable and distinguish it from synthesis- or comparison-oriented siblings.

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 description explicitly enumerates when to call the tool (numerical criteria, clauses/statutes, construction methods, design requirements) and says to prefer it over web search. It also handles the no-data case by instructing the agent not to use low-relevance snippets and to tell the user den lacks the material. It does not explicitly route away from sibling tools such as compare or answer_why, so it stops slightly short of full alternative guidance.

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