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saaskr — Korean SaaS Directory (한국 SaaS 디렉토리)

find_korean_ai_providers

Read-onlyIdempotent

saaskr 한국 SaaS 디렉토리: 다른 서비스에 AI 를 API 로 공급하는 한국 기업(LLM·OCR·음성·번역·임베딩 등)을 찾는다. "한국어 OCR API", "국산 LLM API", "한국어 STT 업체" 처럼 AI 기능을 제품에 넣을 공급사를 찾을 때 사용.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo최대 결과 수(기본 10, 최대 30)
queryNo검색어(선택)
capabilityNo필요한 AI 능력(선택). 가능한 값: llm(LLM(대규모 언어모델)), ocr(OCR·문서 인식), stt(음성 인식(STT)), tts(음성 합성(TTS)), translation(번역), embedding(임베딩·검색), vision(이미지 인식), video(영상 이해·생성), rag(RAG·사내 문서 검색), inference(모델 추론 인프라), agent(AI 에이전트 플랫폼), voice-clone(음성 복제), avatar(AI 아바타·휴먼)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorld=false, so the safety profile is covered structurally. The description adds domain scoping (only AI-API suppliers in Korea) but says nothing about result format, pagination, or caps. With annotations carrying the behavioral burden, this is adequate but not rich.

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?

Two sentences, front-loaded with the directory and scope, then the use case. No filler, though the parenthetical category list partially duplicates the capability enum already in the schema.

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, zero-required-parameter search with no output schema, the description covers what it returns conceptually (a directory lookup of AI suppliers) and when to use it. Missing only minor details like default result caps, which the schema's limit param already states.

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% and the capability parameter already enumerates all thirteen values, so the schema does the heavy lifting. The description echoes a few categories (LLM, OCR, voice, translation, embedding), reinforcing intent but adding no syntax or format detail beyond the schema. Baseline 3 applies.

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 names a specific verb (find) and a tightly scoped resource: Korean companies that supply AI as an API (LLM, OCR, voice, translation, embedding). That scope clearly separates it from broader siblings like search_korean_saas or find_korean_partners, though it does not explicitly name an alternative tool to route against.

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?

It gives clear usage context and concrete query examples ('한국어 OCR API', '국산 LLM API', '한국어 STT 업체') that show when to reach for this tool. However, it never states when NOT to use it or points to a sibling tool for adjacent needs, so it stops short of a 5.

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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