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모집공고 원문 쪽 읽기 — 자격 세부·유의사항 전문

realty_notice_text
Read-onlyIdempotent

입주자모집공고문 원문을 쪽 단위로 읽어 준다.

입주자모집공고문 원문을 쪽 단위로 읽는다 — 팩트시트에 없는 세부(특별공급 소득·자산 기준,
부적격 처리, 계약 유의사항, 옵션 품목 상세)는 이 도구로 원문을 직접 확인하라.
표가 있는 쪽은 pdftotext 특성상 정렬이 깨질 수 있다 — 열 해석이 애매하면 단정하지 말 것.

여러 낱말은 AND로 묶인다('가점제 추첨제'→둘 다 있는 쪽). 0쪽이면 막다르지 않고 낱말별
히트 쪽과 부분일치 상위 쪽을 함께 돌려주니 그걸로 좁혀라(match='any'로 넓힐 수도 있다).
**쪽을 모를 땐 pages_only=true로 먼저 훑어라** — 전문은 한 번에 수만 자다.
전문 응답은 최대 6쪽이고, meta.matching_pages에 일치 쪽 전체 목록이 늘 들어 있다.

Input Schema

NameRequiredDescriptionDefault
matchNo여러 낱말 처리 — all=모두 포함(기본), any=하나라도 포함(넓게 훑을 때)all
pagesNo쪽 범위 직접 지정 (예: '1-3', '44'). **query와 택일이며 함께 주면 거절한다**(error='query_and_pages_conflict') — 종전엔 query를 조용히 버렸다. pages와 함께 준 pages_only는 무의미하므로 무시하고 meta.pages_only_ignored로 실토한다
queryNo찾을 키워드. 공백으로 나눈 낱말을 모두 포함하는 쪽을 찾는다(AND, 공백 무관 매칭) — 예 '가점제 추첨제', '신혼부부 소득'
pages_onlyNo참이면 본문 없이 일치 쪽 번호+발췌만 준다 — 먼저 이걸로 쪽을 고르고 pages로 좁혀 재호출하면 왕복·토큰이 크게 준다
house_manage_noYes공고 관리번호 (realty_presale·realty_notice_facts로 특정)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / pages / description
      Previous value: -"쪽 범위 직접 지정 (예: '1-3', '44'). query와 택일"New value: +"쪽 범위 직접 지정 (예: '1-3', '44'). **query와 택일이며 함께 주면 거절한다**(error='query_and_pages_conflict') — 종전엔 query를 조용히 버렸다. pages와 함께 준 pages_only는 무의미하므로 무시하고 meta.pages_only_ignored로 실토한다"
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
      -  "additionalProperties": true,
      -  "title": "realty_notice_textDictOutput",
      -  "type": "object"
      -}New value: +null
  3. Changed3 schema fields changed
    • addedInput schema / properties / match
      Added value: +{
      +  "default": "all",
      +  "description": "여러 낱말 처리 — all=모두 포함(기본), any=하나라도 포함(넓게 훑을 때)",
      +  "enum": [
      +    "all",
      +    "any"
      +  ],
      +  "title": "Match",
      +  "type": "string"
      +}
    • addedInput schema / properties / pages_only
      Added value: +{
      +  "default": false,
      +  "description": "참이면 본문 없이 일치 쪽 번호+발췌만 준다 — 먼저 이걸로 쪽을 고르고 pages로 좁혀 재호출하면 왕복·토큰이 크게 준다",
      +  "title": "Pages Only",
      +  "type": "boolean"
      +}
    • changedInput schema / properties / query / description
      Previous value: -"찾을 키워드 (예: '신혼부부 소득', '부적격', '발코니 확장') — 등장하는 쪽을 찾아 텍스트를 준다. 공백 무관 매칭"New value: +"찾을 키워드. 공백으로 나눈 낱말을 모두 포함하는 쪽을 찾는다(AND, 공백 무관 매칭) — 예 '가점제 추첨제', '신혼부부 소득'"
  4. Added

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description still adds real behavioral context beyond them: table pages may be misaligned by pdftotext so columns must not be over-asserted, the response is capped at 6 pages, meta.matching_pages always carries the full hit list, and pages_only is silently ignored when pages is given (reported via meta.pages_only_ignored). This is strong disclosure, though the fallback mechanics of the 0-page case are only sketched.

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?

Purpose is front-loaded and the dense body is almost all actionable (conflict error, table caveat, page cap, pages_only-first workflow). It loses a point because the opening line is effectively repeated ('원문을 쪽 단위로 읽어 준다' then '원문을 쪽 단위로 읽는다'), a wasted sentence in an otherwise tight definition.

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?

For a 5-parameter read tool with no output schema and no nested objects, the description covers everything needed to call it correctly: return size limit, where the full matching-page list lives, error naming, fallback behavior, and the pages_only-first workflow. Nothing material is left for the agent to guess.

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 description coverage is 100%, so the baseline is 3 and the schema already documents match, pages, query, pages_only and house_manage_no. The description still adds semantic value the schema does not: the AND semantics of multi-word queries with concrete examples, the workflow of using pages_only to pick pages before narrowing with pages, and the documented fallback result set when a query returns 0 pages.

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?

States a specific verb and resource ('입주자모집공고문 원문을 쪽 단위로 읽는다') and scopes it to page-level reading of the recruitment notice original. It distinguishes itself from the fact-sheet sibling by saying the details absent from 팩트시트 (특별공급 소득·자산 기준, 부적격 처리, 계약 유의사항) are exactly what this tool is for, so an agent can separate it from realty_notice_facts.

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 routing: use this when the fact sheet lacks detail, use pages_only=true first when the page is unknown, and if 0 pages come back, narrow with per-word hits / partial-match pages or widen via match='any'. It also names the query-vs-pages tradeoff and the conflict error, covering when-not and alternatives rather than leaving them to inference.

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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