account_realnameLook up the account holder name of a Korean bank account. 대한민국 은행 계좌의 예금주명을 조회합니다. 송금 전 예금주 확인 등에 사용합니다. bank_code 또는 bank_name 중 하나는 입력해야 합니다. [호출당 60포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| bank_code | No | 은행 코드 (bank_code Tool로 조회 가능, 예: 004) | |
| bank_name | No | 은행명 (예: 국민). bank_code 대신 입력 가능 | |
| account_num | Yes | 계좌번호 (숫자만, 하이픈 제외) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only and open-world, so the baseline is met. The description adds valuable behavioral context: the cost per call ('[호출당 60포인트]') and the required presence of either bank_code or bank_name. It does not detail the return format or error handling, but for a simple lookup this is sufficient.
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 bilingual and includes purpose, usage, a mandatory-input note, and cost in just four sentences. The Korean repetition slightly lengthens it, but every clause carries meaningful information. It is efficient without being terse.
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 simplicity (3 params, no output schema), the description covers the essential aspects: purpose, usage context, the required bank_code/bank_name relationship, and cost. It omits return value details, but the tool's function is straightforward enough that this does not create a significant gap.
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 adds value by explaining that bank_code and bank_name are alternatives and at least one is required, a constraint not present in the schema's required list (which only lists account_num). It also references the bank_code tool for obtaining bank codes, enriching the parameter understanding.
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: 'Look up the account holder name of a Korean bank account.' It uses a specific verb ('look up') and resource ('account holder name'), and includes a usage context ('송금 전 예금주 확인 등에 사용') that distinguishes it from related tools like bank_code or transfer_1won.
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 states when to use it ('before remittance to verify the account holder') and makes a critical input requirement clear: 'bank_code 또는 bank_name 중 하나는 입력해야 합니다' (either bank_code or bank_name must be entered). It does not provide explicit alternatives or when-not-to-use scenarios, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes; even within families like identi_card1-5 vs identi_card_image1-5, the text-input vs image-input distinction is clear. However, the sheer number of tools and some near-synonyms (e.g., ocr_identi1 vs identi_card_image1) could cause occasional misselection, but descriptions mitigate this.
Naming follows a loose verb-first pattern (check_, crawl_, download_, draw_, etc.) but includes significant deviations: bare nouns (bank_code, location, whois), numbered variants (identi_card1, identi_card_image1), and mixed prefixes (ocr_, identity_, etc.). The inconsistency is noticeable but still readable and predictable within functional clusters.
80 tools is far above the typical 3-15, but the server is a broad API aggregator covering many independent domains (banking, ID verification, media conversion, search, LLM, etc.), so the high count is somewhat justified. Still, the sheer number makes the toolkit feel unwieldy and hard to navigate, placing it at the high end of acceptable.
Within its stated purpose as a general-purpose utility API, the toolset covers a wide array of common task families: identity document verification (text and image), OCR field extraction, media conversion, web/search, domain/IP lookup, and LLM chat. Most operations have both get and act variants (e.g., set/get watermark, parcel_tracking/auto), with few obvious dead ends for typical use cases.