venture_biz_infoLook up venture company information of a Korean business, including financial statements and investment data. 벤처기업을 대상으로 사업자 정보, 대차대조표, 손익계산서, 투자정보, 벤처기업확인정보를 조회합니다. [호출당 40포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| biz_no | Yes | 사업자등록번호 (숫자 10자리, 하이픈 제외) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the description adds value by disclosing the specific data returned (financial statements, investment data, confirmation info) and the cost of 40 points per call. This goes beyond the structured metadata.
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 moderately concise but includes redundant bilingual repetition of the same information in English and Korean. The cost note at the end is useful, but the duplication reduces overall conciseness.
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?
For a simple one-parameter read-only lookup, the description covers the core purpose, the returned data categories, and the cost. It doesn't describe output format or error conditions, but annotations and schema cover most essential context, making it fairly complete.
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 fully documents biz_no with a 10-digit, no-hyphen format, so schema coverage is 100%. The description adds no additional parameter syntax or examples; thus the baseline 3 is appropriate.
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 verb 'look up' and the resource 'venture company information of a Korean business', listing financial statements and investment data. The 'venture' qualifier distinguishes it from generic sibling tools like 'biz_detail'.
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 implies usage for venture-company inquiries via the qualifier 'venture company', but it lacks explicit when-not-to-use guidance or alternative tool references. There are no exclusions compared with siblings, leaving usage somewhat implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools target distinct resources and actions, but there is potential overlap between biz_detail and venture_biz_info, and the PCCC tools (req_pccc, get_pccc, check_pccc) require careful reading. Descriptions are clear enough to avoid major misselection.
Tool names mix verb-led patterns (check_*, get_*, req_*, search_*) with noun-phrase patterns (biz_detail, holiday_info, land_rt_price, parcel_tracking). This inconsistency makes it less predictable than a uniform verb_noun scheme, though names remain readable.
With 16 tools, the set is slightly above the typical 3-15 range, but the broad scope of the API—spanning business info, personal validation, car history, parcel tracking, and address search—justifies the count. No tool feels redundant.
The API covers its core verification workflows, but notable gaps exist: no tool for listing parcel carriers despite being referenced in parcel_tracking, no business search by name, and car history only covers flooding and scrap, missing general accident records. These gaps could limit agent workflows.