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saaskr — Korean SaaS Directory (한국 SaaS 디렉토리)

search_korean_saas

Read-onlyIdempotent

saaskr 한국 SaaS 디렉토리: 한국에서 만든 SaaS를 검색한다. 이름·태그라인·소개·태그를 대상으로 하며, 운영 중이 확인된 서비스만 반환한다. 카테고리로 좁힐 수 있다. "한국 AI 도구", "국산 협업툴" 같은 요청에 사용.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo최대 결과 수(기본 10, 최대 30)
queryNo검색어(한국어). 예: "회의록", "결제", "디자인"
categoryNo카테고리 slug로 좁히기. 가능한 값: ai, productivity, social, design, developer-tools, marketing, fintech, ecommerce, education, health, media, lifestyle, hr, gaming, travel, food
kr_featureNo한국 특화 속성으로 좁히기(확인된 서비스만). 가능한 값: tax_invoice(전자세금계산서 발행), kakao_alimtalk(카카오 알림톡 연동), kr_pg(국내 PG 결제(카드·계좌이체)), kr_data_residency(데이터 국내 저장), isms(ISMS/ISMS-P 인증), korean_support(한국어 고객지원), kakao_login(카카오 로그인), naver_login(네이버 로그인), hwp(한글(HWP) 파일 지원)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / kr_feature
      Added value: +{
      +  "description": "한국 특화 속성으로 좁히기(확인된 서비스만). 가능한 값: tax_invoice(전자세금계산서 발행), kakao_alimtalk(카카오 알림톡 연동), kr_pg(국내 PG 결제(카드·계좌이체)), kr_data_residency(데이터 국내 저장), isms(ISMS/ISMS-P 인증), korean_support(한국어 고객지원), kakao_login(카카오 로그인), naver_login(네이버 로그인), hwp(한글(HWP) 파일 지원)",
      +  "type": "string"
      +}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world. Beyond that, the description discloses a meaningful behavioral trait: only services confirmed as operating are returned, and search is scoped to specific text fields.

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

Conciseness5/5

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

Four tight sentences, each earning its place: identity, search scope, return filter, and usage trigger. Front-loaded with what the tool is.

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?

For a read-only search tool with fully documented parameters, the description covers purpose, scope, return semantics, and usage triggers. No output schema exists, but the description states what is returned (운영 중 확인된 서비스), which is adequate; finer return-shape detail is the only minor gap.

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

Parameters3/5

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

Schema description coverage is 100%, with limit, query, category, and kr_feature all documented including enum values in the schema. The description only restates that category narrowing is possible, adding no syntax or format detail beyond the schema — baseline 3.

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

Purpose5/5

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

States a specific verb (검색) and resource (한국 SaaS 디렉토리), plus the exact fields searched (이름·태그라인·소개·태그) and the return filter (운영 중이 확인된 서비스만). This distinguishes it from siblings like get_korean_saas, compare_korean_saas, and find_korean_ai_providers.

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

Usage Guidelines4/5

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

Gives concrete example requests ("한국 AI 도구", "국산 협업툴") that signal when this tool applies, and notes category narrowing. It does not explicitly name alternative tools or exclusions, but the triggering context is clear.

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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