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socialkim

korea-onegov-mcp

by socialkim

정부 공개자료 통합검색

onegov_search
Read-onlyIdempotent

Search Korean government documents from 50 agencies using natural language queries, with filters for document type, ministry, region, and more.

Instructions

대한민국 중앙행정기관 약 50곳의 보도자료·정책·법령·통계·정보공개·자주묻는질문·고시공고를 한 번에 검색한다(약 9만 건 색인).

부처 이름을 몰라도 자연어로 검색된다. 하이브리드(키워드+의미) 검색이 기본.

Args:

  • query (string, 선택): 검색어. 비우면 필터 조건만으로 최신 문서를 훑는다. 짧고 일반적인 명사구가 잘 맞는다(예: "청년 월세", "반도체 특별법").

  • doc_type (string[], 선택): press_release | policy | law | statistic | info_disclosure | faq | notice

  • ministry (string[], 선택): 기관코드(예: MSS=중소벤처기업부, MOLIT=국토교통부). 코드는 onegov_list_ministries 로 확인.

  • category (string[], 선택): 콘텐츠 분류. 지원사업은 "grant".

  • region (string[], 선택): 지역명(예: "서울특별시", "경기도", "전국").

  • law_ref (string[], 선택): 관련 법령명 정확일치(예: "중소기업창업 지원법").

  • from / to (YYYY-MM-DD, 선택): 발행일 범위.

  • sort ('relevance'|'date_desc'|'date_asc', 기본 relevance): 정확도순 / 최신순 / 오래된순.

  • mode ('hybrid'|'fulltext'|'semantic', 기본 hybrid): 검색 방식.

  • page (number, 기본 1) / size (number, 1~50, 기본 20): 페이지네이션.

  • include_facets (boolean, 기본 true): 결과 분포(문서유형·기관·연도·지역·법령) 요약 포함 여부.

  • response_format ('markdown'|'json', 기본 markdown)

Returns: markdown: 총 건수 + 결과별 제목/유형/기관/발행일/문서 id/원문 링크/요약, 그리고 패싯 요약. json: { query, page, size, total, results[], facets{}, meta{} } — results[i]에 id, doc_type, title, summary, ministry_code, ministry_name, published_at, source_url, kogl_type, law_ref[], region, attachments[], score 포함.

Examples:

  • "작년에 나온 청년 주거 지원 정책" → query="청년 주거 지원", from="2025-01-01", to="2025-12-31"

  • "중기부가 낸 창업 공고만" → query="창업", ministry=["MSS"], doc_type=["notice"]

  • "개인정보보호 관련 법령 최신순" → query="개인정보", doc_type=["law"], sort="date_desc"

  • 쓰지 말 것: 문서 전문이 필요할 때(→ onegov_get_document), 마감임박 지원사업 목록(→ onegov_deadline_grants)

Errors:

  • 잘못된 doc_type/sort 값은 어떤 필드가 틀렸는지 알려준다.

  • 429는 레이트리밋이므로 잠시 후 재시도.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo발행일 종료(YYYY-MM-DD)
fromNo발행일 시작(YYYY-MM-DD)
modeNo검색모드: hybrid(기본)|fulltext|semantic
pageNo페이지 번호(1부터)
sizeNo페이지당 건수(최대 50)
sortNo정렬: relevance(기본)|date_desc|date_asc
queryNo검색어. 비우면 필터 조건만으로 조회한다.
regionNo지역명 배열(예: ['서울특별시']).
law_refNo관련 법령명 배열(정확일치).
categoryNo콘텐츠 분류 배열. 지원사업 공고는 'grant'.
doc_typeNo문서유형 필터(여러 개 가능). press_release=보도자료, policy=정책, law=법령, statistic=통계, info_disclosure=정보공개, faq=자주묻는질문, notice=고시·공고
ministryNo기관코드 배열(예: ['MSS','MOLIT']). onegov_list_ministries 로 코드 확인.
include_facetsNo결과 분포 요약 포함 여부
response_formatNo출력 형식. 'markdown'은 사람이 읽기 좋은 요약(기본값), 'json'은 API 원본에 가까운 구조화 데이터.markdown
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds operational details: index size (~90k), hybrid search, error handling for invalid enums (field-specific error) and rate limits (429 retry). This goes beyond annotations but does not contradict them.

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?

Well-structured with clear sections (Args, Returns, Examples, Errors). Front-loaded with the main purpose. However, it is somewhat lengthy; some sentences could be condensed without losing meaning.

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?

With 14 parameters and no output schema, the description fully documents return formats (markdown with total/results/facets, JSON with detailed fields including score), provides multiple examples, and covers error cases. An AI agent has sufficient context to use the tool correctly.

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%, but the description adds substantial value: it explains query style ('짧고 일반적인 명사구가 잘 맞는다'), provides Korean names for doc_type, references onegov_list_ministries for ministry codes, gives example values like 'grant' for category, and describes pagination limits. This enriches understanding beyond 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 searches across ~50 Korean government agencies' press releases, policies, laws, statistics, etc. It distinguishes from sibling tools like onegov_get_document (full document retrieval) and onegov_deadline_grants (deadline grants).

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?

Explicit examples are given for different use cases (e.g., '청년 주거 지원 정책', '중기부가 낸 창업 공고만'). It also explicitly states when NOT to use (full document → onegov_get_document, deadline grants → onegov_deadline_grants).

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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