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korean-law-alio-mcp

by scvcoder

compare_alio_regulations

Compare regulations across Korean public institutions on specific topics such as blind hiring, leave, or discipline. Automatically match institutions or specify which ones to include.

Instructions

[ALIO] 토픽(블라인드채용·휴직·징계 등) 기준 기관간 규정/조문 비교. institutions 생략 시 수집된 전체 기관 자동. 사용자가 'A·B·C 기관과 비교' 같이 특정하면 해당 명칭/코드를 institutions 에 전달.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes비교할 주제 키워드 (예: '블라인드 채용', '휴직', '징계')
institutionsNo비교 대상 기관코드/기관명 (선택). 생략 시 수집된 전체 기관에서 토픽 매칭 자동 추출. 사용자가 'A, B 기관과 비교'처럼 특정하면 해당 명칭/코드를 배열로 전달.
maxPerInstitutionYes기관당 최대 히트 규정 수
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It only describes basic functionality (comparing regulations by topic) and the institutions automatic behavior. It does not disclose whether the operation is read-only, destructive, requires authentication, or any side effects. No contradiction with annotations since none exist.

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 concise: one main sentence in Korean followed by an explanatory note about institutions. It is front-loaded with the core purpose. Could be slightly more structured with separate sentences for each aspect, but overall efficient.

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?

With 3 parameters, no output schema, and no annotations, the description covers purpose and parameter usage but lacks details about the output format (e.g., what is returned: list of regulation texts? titles?), exact matching behavior, or any constraints. It is adequate but could benefit from more comprehensive context.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for all three parameters. The description adds significant context for the 'institutions' parameter, explaining that omitting it triggers automatic inclusion of all collected institutions, and users can specify by name/code. This meaningfully supplements the schema.

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 compares regulations/articles across institutions by topic, with examples like blind recruitment, leave, discipline. It uses a specific verb ('비교' = compare) and resource ('규정/조문'), and distinctly differs from siblings like compare_alio_articles (which compares individual articles) and search tools.

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 omit institutions (automatic use of all collected) and when to specify (user mentions specific institutions). It provides clear context for the institutions parameter but does not explicitly exclude alternatives like compare_alio_articles or other similar tools, missing a when-not-to-use note.

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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