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scvcoder

korean-privacy-law-mcp

by scvcoder

verify_pipa_citation

Verify Korean privacy law citations for accuracy. Detects hallucinations by checking existence of law, article, paragraph, and subparagraph. Supports aliases and temporal verification.

Instructions

법령 인용 환각 검증 (4계층: 법령 존재 → 조문 존재 → 항 존재 → 호·목 존재). 법제처 API에 실제 존재하는지 사실 검증 — LLM이 못 하는 도구 핵심 가치. 예: 'PIPA §9999' → 조문 없음 [HALLUCINATION_DETECTED] / 'PIPA §15 ① 6호' → ✓ 모든 단계 검증. as_of 지정 시 시점 기준 검증 (정통망법 §22 폐지 여부 등). 법령명 약칭(PIPA·개보법·정통망법 등) PRIVACY_ALIASES 자동 정규화. 환각 발견 시 [HALLUCINATION_DETECTED] 마커 + 정확한 근거 + 다음 도구 안내 자동. 다음: search_law(법령명)으로 정식명, get_law_text(mst)로 본문 직접 확인.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
citationYes검증할 인용 문자열. 다양한 약식 수용 — 'PIPA §15 ① 6호', '개인정보 보호법 제15조제1항제6호', '정통망법 §22', '안전성 확보조치 기준 제5조' 등. 법령명 약칭(개보법·정통망법·통비법 등)은 PRIVACY_ALIASES로 자동 정규화.
as_ofNo시점 YYYYMMDD (선택). 지정 시 그 시점 본문 기준 검증. 예: '20190601' = 2019.6.1 시점에 정통망법 §22 유효 여부.
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It details the verification steps, automatic alias normalization, hallucination markers, and temporal verification via as_of. It does not mention error handling or side effects, but the core behavior is transparent.

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?

The description is information-dense and well-structured, starting with the core purpose and hierarchy, then detailing examples, temporal feature, alias handling, and next steps. It could be slightly more concise but avoids unnecessary repetition.

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

Completeness3/5

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

While the description mentions hallucination markers and guidance, it does not fully specify the complete response format or structure for successful verifications. Given no output schema, more detail on the return value would improve completeness.

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?

The description adds significant meaning beyond the input schema by explaining how the citation string is processed, providing concrete examples, and describing automatic alias resolution and temporal verification. It fully compensates for the schema's minimal descriptions.

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?

The description clearly states it verifies legal citations using a 4-tier hierarchy (law, clause, paragraph, subparagraph) and explicitly distinguishes from sibling tools like search_law and get_law_text by highlighting that it detects hallucinations LLMs cannot.

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?

The description explains when to use the tool (to verify citations and detect hallucinations) and provides guidance on next steps with sibling tools. However, it does not explicitly state when not to use it, though the purpose is sufficiently 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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