check_phone_validValidate and format a phone number with local numbering metadata. 전화번호의 국가, 형식 유효성, 국내 010 모바일 여부를 외부 API 없이 분석합니다. [호출당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | 전화번호 (예: 01012341234, 해외는 +국가코드 형식) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds valuable behavior beyond that: it analyzes without external API and reveals the specific metadata areas (country, format validity, 010 mobile). It also notes the per-call cost of 10 points, which is useful operational context. No contradictions with annotations.
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, with one English sentence and one Korean sentence that complement each other—the English gives the high-level action, the Korean adds specific analysis categories and the cost note. No redundancy; every sentence earns its place.
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 that the tool has no output schema, the description reasonably outlines what the user will get: validation, formatting, and analysis of country, format validity, and mobile status. It also notes no external API and cost. While it lacks exact output structure, it is sufficient for a simple one-param utility.
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 input schema covers 100% of the single param with a clear description and example. The tool description adds 'local numbering metadata' but does not provide additional syntax or format details beyond the schema. Thus baseline 3 is appropriate, as the schema does the heavy lifting.
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 'Validate and format a phone number' and further specifies analyzing country, format validity, and domestic 010 mobile status with local numbering metadata. This distinguishes it from sibling tools like check_email_valid and check_spam_number by focusing on phone number validation and formatting.
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 this tool: to validate and format phone numbers, including international numbers with +country codes. It does not explicitly mention alternatives or exclusions, but the purpose is unambiguous, and sibling tools like check_spam_number cover different use cases, so no confusion arises.
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.