scienceON_collect_groups

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

The description goes well beyond the annotations by explaining key behavioral details: save=true writes a file and returns a path, while save=false returns records directly but only the first 100 ('앞 100건만 싣는다'). It also warns about meta.truncated and explains how to resolve truncation by raising max_records above meta.union_upper_bound. This provides actionable insight into side effects and output limits not present in the 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 well-organized and front-loaded with the core purpose. It uses bullet-like explanations for the group structure, a concrete example, and a clearly marked warning. Every sentence adds value; no filler. Despite being longer than average, it remains scannable and information-dense.

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 tool's complexity (10 parameters, nested groups) and the absence of an output schema, the description does a solid job of explaining the main flow, output modes, and truncation semantics. It falls slightly short on fully documenting every parameter, but the core behavior is well covered and the example clarifies the most complex part.

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?

The schema has 0% description coverage, so the description must compensate. It thoroughly explains the 'groups' parameter with its nested fields (field, terms, contains, lang, max) and provides a JSON example. It also clarifies 'save' behavior. However, other parameters like target, formats, out_dir, year_from, year_to, and retry_incomplete are left unexplained, though some are self-explanatory from their names.

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 clearly states the tool's function: merging multiple search groups into a single corpus with deduplication ('여러 **검색 그룹**을 한 코퍼스로 합쳐 수집(CN 중복제거)'). It uses a specific verb (collect/merge) and resource (search groups into corpus), and this clearly distinguishes it from siblings like scienceON_search, scienceON_export, and scienceON_status.

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 explains the intended use case: building corpora that cannot be created with a single query ('단일 검색어로는 못 만드는 코퍼스를 만든다'). It provides a concrete example with different fields and filters. It does not explicitly mention alternative tools or when not to use it, but the use case is clear from the contrast with single-query search.

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