search_container_record

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals essential traits: the dataset scale, that each call requests a page from odcloud, that filters are applied as partial-match post-filtering within the fetched page (a critical limitation for agents filtering large data), and that per_page is capped at 1000. This goes well beyond a generic search description and gives agents operational awareness that affects how they paginate and filter.

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 three concise, information-dense sentences. The first states purpose and scale, the second explains the paging model and post-filtering behavior, and the third sets the per_page limit. There is zero fluff – every sentence contributes to agent decision-making.

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 no output schema and no annotations, the description covers core usage: pagination, filter behavior, and limits. The total count (166,275) gives scale awareness. It doesn't specify the return fields or how to determine when pagination ends, but for a page-listing tool an agent can infer a list of records. Minor gap in response structure, but overall it provides enough to call the 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 0%, so the description must compensate. It does by explicitly naming all four filter parameters (container_number, wagon_number, receipt_date, item_name) and clarifying page/per_page via 'page units' and 'per_page max 1000'. It also explains that filters are partial-match post-filters, adding semantic value not present in the schema. Every parameter is addressed.

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 '컨테이너 적재 이력 페이지 조회' – a specific verb ('조회' = inquiry) plus a precise resource ('container loading history page'). This clearly identifies what the tool does and distinguishes it from sibling search tools like search_freight_code or search_station, as it targets container loading records with pagination.

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 strong usage context: it is for large-volume (166,275 records) container loading history, retrieved page-by-page from odcloud, with optional post-filtering. It doesn't explicitly state when to use it over alternatives, but the operational details make the intended scenario clear. No exclusion criteria are mentioned, but the context is sufficient for an agent to select it appropriately.

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