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명품·백화점·면세점 판매직 연봉 벤치마크

get_salary_benchmark
Read-onlyIdempotent

[메종 드 탤런트 명품 매장 채용] 한국 럭셔리 리테일 매장직 연봉 벤치마크(만원, 연봉 기준)를 업종·경력별로 조회합니다. Salary benchmarks for Korean luxury/department store/duty-free retail staff by category and experience. 업종: luxury=럭셔리 패션, cosmetics=뷰티/화장품, jewelry=시계/주얼리, fashion=패션/의류, food=프리미엄 F&B, department=백화점, dutyfree=면세점, lifestyle=라이프스타일. 경력: entry=신입(0년), junior=주니어(13년), mid=미들(35년), senior=시니어(5~10년), lead=리드(10년+).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYes업종 코드
experienceNo경력 코드(생략 시 전 구간)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely useful behavioral context beyond them: the return units are 만원 and the basis is annual salary (연봉 기준), and it discloses that omitting experience returns all ranges (생략 시 전 구간).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose, then the enum glossary. The Korean and English sentences are somewhat redundant, but the enum definitions each carry information, so there is little waste overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and only two parameters, the description supplies what an agent needs: scope, filterable dimensions, enum meanings, and the unit/basis of returned values (만원, annual). The precise return structure is unspecified, which is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the schema only labels the enums tersely ("업종 코드", "경력 코드"). The description explicitly expands every enum value (luxury=럭셔리 패션 … lifestyle=라이프스타일; entry=신입(0년) … lead=10년+), adding real meaning the schema lacks.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 조회 (lookup) of 연봉 벤치마크 (salary benchmarks) by 업종·경력 for Korean luxury retail staff. It is clearly distinct in resource from siblings get_job/search_jobs, though it never names those siblings explicitly to route the agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the domain (query compensation data for a category), but there is no explicit when-to-use, when-not-to-use, or reference to the sibling tools get_job/search_jobs. An agent can infer the intent but receives no routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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