k_snippets

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It explains the return format (original clause text plus source identifiers like KDS 14 20 22 §4.3.1), the meaning of relevance=low and lacks_answer=true, and instructs the agent to say den does not hold the material rather than fabricating. It also discloses as_of behavior for historical queries.

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 front-loaded with the core purpose, then organized into scannable use-case categories, return semantics, missing-data handling, and historical querying. Though longer than average, each section earns its place and the structure makes it easy for an agent to extract the key guidance.

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?

The schema covers all five parameters, and the description covers purpose, when to use it, return format, source citation expectations, missing-data behavior, and historical query semantics. With no annotations and an output schema present, nothing critical is missing for selecting and invoking this tool correctly.

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 100%, so the baseline is 3; the schema already documents question, as_of, limit, scope, and profile in detail. The description adds only a small amount of behavioral context around as_of and scope, but does not materially improve parameter understanding beyond the schema.

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 with a specific verb and resource: it finds original numeric text and clause wording from Korean construction standards (KDS, KCS, KS) and building laws, and gives four concrete example categories. It is clear what the tool does, but it does not explicitly distinguish this tool from sibling tools such as compare, enumerate, or traverse, so it does not fully meet the 5-level criterion.

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 gives explicit use contexts with concrete examples ('철근 피복두께', '이어치기 면 처리') and instructs to use this tool before web search when grounding answers. It does not provide exclusions or mention when sibling tools would be more appropriate, so it is clear context without explicit alternatives.

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