search_jusoSearch Korean road-name addresses by keyword. 지번 또는 도로명 키워드로 도로명 주소를 검색합니다. 페이지당 10건씩 반환되며 total_count 필드로 전체 검색결과 개수를 확인할 수 있습니다. [호출당 2포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| juso | Yes | 검색할 주소 키워드 (지번, 도로명. 예: 디지털로) | |
| page | No | 검색 결과 조회 페이지 (기본값 1, 페이지당 10건) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and openWorldHint annotations, the description discloses pagination behavior (10 per page), the total_count field for result totals, and a cost of 2 points per call. This adds practical context that helps the agent anticipate response characteristics.
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 concise and front-loaded, with two sentences covering purpose, scope, and pagination, plus a cost note. Every sentence provides useful information without redundancy.
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 read-only search tool with two parameters and no output schema, the description covers pagination and total_count, giving insight into the response structure. It does not detail all return fields, but that is not essential for this basic tool.
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%, with both juso and page fully described. The description does not add additional parameter information beyond what the schema already provides, so the baseline of 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 tool searches Korean road-name addresses by keyword, using specific verbs and resources. It distinguishes itself from sibling tools by being the only address search tool, and mentions pagination and total_count for result management.
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 searching Korean addresses but provides no explicit guidance on when to use this tool versus alternatives. It does not mention exclusions or alternative tools, so the agent must infer usage from the purpose.
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.