holiday_infoLook up Korean public holidays for a given year and month. 해당 년월의 대한민국 공휴일 정보를 조회합니다. 영업일 계산, 일정 관리 등에 사용합니다. [호출당 3포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | 조회 년도 (1900 ~ 2200, 예: 2024) | |
| month | Yes | 조회 월 (1 ~ 12, 예: 02) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe read operation. The description adds a cost note ('[호출당 3포인트]' – 3 points per call) and implies a lightweight query. It does not contradict the annotations, and the additional cost info is useful 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 extremely concise, front-loaded with the core purpose, and each sentence provides value: the English summary, a Korean translation for accessibility, use-case context, and a cost hint. No redundancy or fluff.
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?
This is a simple two-parameter read-only tool with no output schema. The description adequately conveys the tool's purpose and scope, and with the schema and annotations, an agent has enough context to invoke it correctly. It could be improved by stating the return format (e.g., list of holiday names and dates), but given the low complexity, the current level is sufficient.
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 provides 100% coverage for both parameters (year and month), including ranges and examples, so the description has little to add. The description only rephrases 'given year and month' without offering additional format or behavior insights. This is at the baseline for full schema coverage.
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 Korean public holidays for a given year and month.' This is a specific verb (look up) + resource (Korean public holidays) with a defined scope (year/month). It also mentions intended use cases (business day calculation, schedule management), which helps differentiate it from sibling lookup tools like bank_code or land_rt_price.
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 provides clear context for when to use the tool: '영업일 계산, 일정 관리 등에 사용합니다' (used for business day calculation, schedule management, etc.), implying the tool is appropriate for scheduling and business-day-related tasks. However, it does not explicitly mention alternative tools or conditions for when not to use it, so it lacks explicit exclusions.
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.