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verify_citations

[KEY REQUIRED · 1 credit] 아무 텍스트에 담긴 한국 법률 인용을 검증합니다 — 다른 LLM이 쓴 답변도 됩니다. 조문과 사건번호를 전부 추출해 검증 자산과 대조하고, 인용별 상태(verified_in_corpus 본문까지 대조 / verified_in_index 정본 색인에서 실재 확인 / absent_in_index 그 법령을 결번 없이 아는데 이 조문이 없음 / not_in_corpus 덮지 못함)를 출처 링크와 함께 돌려줍니다. 1회에 크레딧 1개를 사용합니다. LLM 미호출·결정론. 주의: not_in_corpus는 '이 코퍼스에 없다'는 뜻이지 '지어낸 인용'이라는 증명이 아닙니다 — law.go.kr에서 직접 확인하라는 표시입니다. 인용 실재(L1) 외에 네 축을 더 돌립니다: doctrine(법리 모순 — 조문이 실재해도 적용이 어긋나는 경우), currency(폐지·개명된 법령 인용), precedent_currency(이후 전원합의체가 견해를 변경하며 참조한 판례), support(인용 조문 본문에 그 수치가 실제로 있는가). 각 축은 검사 범위(scope)와 분모(examined·known_count·index_size)를 함께 냅니다 — findings 0 은 '정확하다'가 아니라 '그 축에서 안 잡혔다'는 뜻입니다. 전부 결정론이며 LLM을 부르지 않습니다. / Fact-check the Korean legal citations inside any text — including text another LLM produced. Returns per-citation status (verified_in_corpus = body matched, verified_in_index = existence confirmed in the authoritative index, absent_in_index = the law is indexed gaplessly and this article is not in it, not_in_corpus = not covered) with source links. Costs 1 credit per call. Deterministic, no LLM. IMPORTANT: Beyond citation existence (L1) it runs four more axes: doctrine (misapplied rule even when the provision exists), currency (repealed or renamed statutes), precedent_currency (cases referenced by a later en banc decision that states a change of position), and support (whether the figure claimed actually appears in the cited provision's text). Each axis reports its scope and denominator — findings==0 means 'nothing caught on that axis', not 'correct'. All deterministic, no LLM call. 'not_in_corpus' means the citation is NOT in this corpus — it is a flag to check manually at law.go.kr, not proof that the citation is fabricated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText containing Korean legal citations (max 20000 chars).
client_tagNo선택. client_tag: 호출자 식별용, **응답에 영향 없음**(8~64자, 영숫자·_-.). 서버 로그에만 남아 호출을 골라 세는 데 씁니다. 형식이 어긋나면 무시합니다 — 거절하지 않습니다. 질의 본문에 섞지 마세요: 본문에 넣으면 질문의 일부가 되어 분류와 검증 결과가 달라집니다. / Optional caller label (8-64 chars). Logged only; never affects the response. Invalid values are ignored, not rejected. Do not put it in the query text — there it becomes part of the question.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / client_tag
      Added value: +{
      +  "description": "선택. client_tag: 호출자 식별용, **응답에 영향 없음**(8~64자, 영숫자·_-.). 서버 로그에만 남아 호출을 골라 세는 데 씁니다. 형식이 어긋나면 무시합니다 — 거절하지 않습니다. 질의 본문에 섞지 마세요: 본문에 넣으면 질문의 일부가 되어 분류와 검증 결과가 달라집니다. / Optional caller label (8-64 chars). Logged only; never affects the response. Invalid values are ignored, not rejected. Do not put it in the query text — there it becomes part of the question.",
      +  "type": "string"
      +}
  2. Added

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and handles it well: it discloses deterministic/no-LLM behavior, the 1-credit cost, per-citation statuses, the four additional axes, scope/denominator reporting, and the important caveat that not_in_corpus is not proof of fabrication.

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

Conciseness3/5

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

The description is information-dense and front-loads key operational facts, but the Korean and English sections duplicate nearly all of the same content, making it longer than necessary. A tighter single-language version would earn higher marks.

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

Completeness5/5

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

Even without an output schema, the description explains what is returned (per-citation statuses, source links, axes, scope/denominators) and how to interpret ambiguous results. For a complex verification tool, this is complete enough for an agent to invoke it correctly.

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%, so the input schema already fully documents text and client_tag. The tool description adds no parameter-specific detail beyond what the schema provides, so it stays at the baseline.

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 states a specific verb (verify/fact-check), a clear resource (Korean legal citations inside any text), and enumerates the detailed status taxonomy and four extra verification axes. This makes the tool instantly distinguishable from sibling lookup/search tools even without naming them.

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: accepts any text, including text produced by another LLM; specifies what not_in_corpus means and directs manual follow-up at law.go.kr; and clarifies that findings==0 means 'nothing caught,' not 'correct.' It stops short of explicitly naming alternatives or stating when not to use the tool.

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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