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buenosiempre-cmd

dongguk-rule-mcp

by buenosiempre-cmd
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

verify_rule_citations

Read-onlyIdempotent

Verify citations of Dongguk University regulations in a document against the official rule book, confirming regulation existence, article numbers, titles, and clause references.

Instructions

기안문·품의서·공문·AI 답변 텍스트에 인용된 동국대학교 규정 조문을 실제 규정집과 대조 검증하는 환각 게이트. 「규정명」 제N조(제목) 제N항 표기를 추출해 규정 실존, 조문 실존(본칙 존재 범위 안내), 조문 제목 일치, 항 번호까지 확인합니다. 결재 전 인용 점검에 사용하세요. 규정 내용 질문에는 lookup_dongguk_rule을 사용하세요.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes검증할 전체 텍스트 (20,000자 이하). 예: 품의서 본문
campusNo업무 캠퍼스 맥락 all/seoul/wise. 공식 목록에는 캠퍼스 필터가 없어 전체 검색 후 적용범위를 확인합니다.all

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes도구 실행 성공 여부
dataNo성공 시 구조화 결과
toolYes실행한 도구 이름
errorNo실패 시 구조화 오류

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.0

TDQS

A5/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, and open-world behavior, and the description adds concrete behavioral detail: it extracts rule/article/clause citations, checks existence, article title match, clause number, and explains campus scope handling.

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?

The description is compact and information-dense, with no redundant wording. It efficiently combines purpose, usage guidance, and behavioral details in two sentences plus clear parameter descriptions.

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?

Given the output schema exists, the description provides sufficient context: purpose, usage boundaries, parameter behavior, and relationship to sibling tools. It does not need to restate return values and covers the operational nuances relevant to an agent.

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?

Schema coverage is 100%, and the descriptions add meaningful details: text has an explicit length limit and example, while campus explains its default, allowed values, and the behavior that campus is not an official filter and requires post-search scope verification.

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 the tool's purpose: verifying citations to Dongguk University regulations in documents/AI responses against the actual rulebook. It also distinguishes itself from lookup_dongguk_rule by explicitly directing content questions to that sibling tool.

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

Usage Guidelines5/5

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

The description explicitly says to use this tool for citation checks before approval and explicitly says to use lookup_dongguk_rule for rule content questions, providing clear when-to-use and when-not-to-use 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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