json_to_excelConvert JSON data into an Excel (XLSX) file. JSON 데이터를 EXCEL(XLSX) 파일로 변환해 반환합니다. data_list 는 객체 배열([{"컬럼":"값", ...}, ...]) 또는 2차원 배열([[...], ...]) 형식을 지원합니다. [호출당 1포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| data_list | Yes | 변환할 데이터 목록. 객체 배열 또는 2차원 배열 (2차원 배열은 모든 행의 열 개수가 같아야 함) | |
| sheet_name | No | 엑셀 시트 이름 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation as safe (readOnlyHint=true, openWorldHint=false). The description adds behavioral context by noting that the tool returns the converted file and that each call costs 1 point. It does not contradict annotations and provides useful operational details.
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, front-loads the core purpose, and includes only essential details: the conversion operation, supported formats, and the per-call cost. The bilingual repetition serves user needs and does not pad the description with unnecessary words.
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 2-parameter tool with no output schema, the description sufficiently covers functionality, input formats, and the fact that the file is returned. It does not mention potential file-size limits or error handling, but these are minor for this use case.
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 schema already documents both parameters. The description's mention of data_list formats mirrors the schema and adds a concrete example of the expected structure, but it does not significantly extend beyond the schema. Thus, 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 tool's function: 'Convert JSON data into an Excel (XLSX) file.' This is a specific verb+resource pair, and no sibling tool overlaps with this functionality. The bilingual phrasing reinforces the purpose without ambiguity.
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 on when to use the tool (to convert JSON to Excel) and mentions the supported data formats (object array or 2D array). It does not explicitly mention when-not-to-use or alternatives, but the absence of competing sibling tools makes this acceptable.
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.