parcel_tracking_autoTrack a Korean parcel in real time with automatic carrier detection from the tracking number alone. 택배사 지정 없이 운송장번호만으로 택배사를 자동 판별해 실시간 배송현황을 조회합니다. 택배사를 이미 아는 경우에는 parcel_tracking Tool이 더 정확합니다. [호출당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| trackingNumber | Yes | 운송장번호 (택배사 지정 없이 형식만으로 자동 판별) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds behavioral context such as the cost of 10 points per call and the real-time nature of the tracking data. It does not contradict the annotations and provides useful non-obvious details 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 front-loaded with the core function and includes essential usage guidance and cost. However, it repeats the same sentence in English and Korean, creating redundancy that slightly reduces conciseness. Overall it remains brief and structured but loses a point for duplication.
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 single-parameter tool with no output schema. The description mentions the output type (real-time delivery status), the cost, and the alternative tool when the carrier is known. It does not explain error handling or edge cases, but given the low complexity, the coverage is adequate for an agent to select and invoke it correctly.
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 100% coverage for the single trackingNumber parameter, and the schema description already explains automatic format-based carrier detection. The tool description reiterates this but does not add new parameter-specific information, so it meets the baseline without exceeding it.
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 tracks Korean parcels in real time with automatic carrier detection from the tracking number alone. It distinguishes itself from the sibling parcel_tracking tool by noting that parcel_tracking is more accurate when the carrier is already known, which clarifies its specific scope.
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?
Explicitly states when to use this tool (when the carrier is unknown) and when not to (if the carrier is known, use parcel_tracking instead). This provides clear contextual guidance and names the alternative, making the usage decision straightforward.
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.