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socialkim

korea-onegov-mcp

by socialkim

지원사업·공고 찾기

onegov_find_support_programs
Read-onlyIdempotent

Search Korean government support programs (grants) and public notices by topic, region, and ministry. Filter results by date and sort order to find relevant opportunities.

Instructions

지원사업(category=grant) 과 고시·공고(doc_type=notice) 로 범위를 미리 좁혀 검색하는 편의 도구. "창업 지원금", "소상공인 지원사업" 같은 요청에 바로 쓴다.

onegov_search 에 category=["grant"] 를 매번 넣는 것과 같지만, 필요한 필터만 노출해 실수를 줄인다.

Args:

  • query (string, 필수): 무엇에 대한 지원사업인지(예: "청년 창업", "수출", "AI").

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

  • ministry (string[], 선택): 기관코드.

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

  • include_notices (boolean, 기본 true): 지원사업 분류가 안 붙은 일반 고시·공고까지 함께 볼지 여부.

  • sort ('relevance'|'date_desc'|'date_asc', 기본 date_desc): 기본은 최신순.

  • page / size / response_format: onegov_search 와 동일.

Returns: onegov_search 와 동일한 형식.

Examples:

  • "요즘 나온 청년 창업 지원사업" → query="청년 창업"

  • "경기도 소상공인 지원" → query="소상공인", region=["경기도"]

  • 마감일 기준으로 급한 것부터 보려면 onegov_deadline_grants 를 먼저 쓸 것.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo발행일 종료(YYYY-MM-DD)
fromNo발행일 시작(YYYY-MM-DD)
pageNo
sizeNo
sortNo정렬(기본 최신순)date_desc
queryYes지원사업 주제
regionNo지역명 배열
ministryNo기관코드 배열
include_noticesNo일반 고시·공고까지 포함할지 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 indicate read-only, idempotent, non-destructive behavior. Description adds that return format matches onegov_search and explains the include_notices default, but no additional behavioral context beyond what annotations provide.

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?

Description is well-structured with clear sections (purpose, comparison, args, examples) and front-loaded with core purpose. Slightly long but each sentence adds value; no wasted words.

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

Completeness4/5

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

With 10 parameters and no output schema, description covers all parameter explanations, return format (same as onegov_search), and usage context. Examples enhance completeness. No gaps noted.

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 80%, and description adds meaning beyond schema by explaining parameter purpose (e.g., include_notices, sort options with defaults) and providing examples for query and region. Some parameters like page and size are less elaborated but schema covers them.

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?

Description clearly states it's a convenience tool for searching support programs and announcements, explicitly distinguishing it from onegov_search by pre-filtering category and doc_type. Examples illustrate typical usage.

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?

Explicitly says when to use (support program/announcement searches) and compares to onegov_search. Provides an alternative tool (onegov_deadline_grants) for deadline-urgent requests, clearly guiding agent decision.

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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