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korea-realty

by app.sallim

경매 사건 상세

fetch
Read-onlyIdempotent

search가 돌려준 id로 경매 사건의 전체 내용을 가져온다.

id 형식은 "법원명|사건번호" (예: "서울동부지방법원|2025타경51727").
사건번호는 법원 간 중복되므로 법원명 없이 조회하면 후보 목록이 돌아올 수 있다.

rights(매각물건명세서 요약)가 있으면 법원 공시의 전달로만 인용하고, 없으면(rights_note
참조) 권리관계를 지어내지 말 것 — 권리분석 판단은 이 도구의 데이터 밖이다.

**이 축의 자리** — 사건 상세도 둘이고 입력과 응답 형태로 갈린다. 이 도구는 `search`가
준 `"법원명|사건번호"` 한 덩어리를 그대로 받아 **문서형 텍스트**를 돌려준다(ChatGPT
커넥터 규격). 사용자가 사건번호를 말로 불러줬거나, `rights`·`min_bid_source` 같은 필드를
**값으로** 다뤄야 하면 `realty_get_auction_case`(사건번호·법원명을 따로 받는 구조화 응답)를
써라. 유찰 이력·기일별 저감은 어느 쪽도 아니고 `realty_auction_history`다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYessearch가 돌려준 id를 **그대로** 넣는다 — `법원명|사건번호` 형식이다 (예: '서울동부지방법원|2025타경51727'). 파이프 앞은 정식 법원명이고 지원은 '진주지원'처럼 지원명만 오는 행도 있다. 파이프 뒤는 `2026타경3571` 꼴 (연도 4자리 + '타경' + 일련번호, 일련번호는 3~6자리). 법원명 없이 사건번호만 넣으면 사건번호가 법원 간 유일하지 않아 후보 목록이 돌아온다

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. Beyond that, the description adds substantial behavioral detail: it returns a document-style text rather than structured fields, may produce candidate lists when court name is omitted, and instructs the agent to treat rights data as a court citation only, not to invent relations. This goes well beyond default annotation semantics.

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 well-structured: a core opening sentence, then id format, then important behavioral caveats, then a disambiguation paragraph. Every chunk has a purpose. It is a bit longer than strictly necessary, and it repeats some id-format details already present in the schema, but the payout is rich guidance in return for the length.

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 single-parameter, read-only tool with no output schema, the description covers the input origin, id format, fallback behavior, the nature of the response (document-form text), and clear differentiation from the two nearby alternatives. This is enough for an agent to invoke the tool correctly and interpret its results in context.

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

Parameters3/5

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

The only parameter, id, is already thoroughly described in the input schema with format, examples, and the no-court name caveat. Schema coverage is at 100%, so the description's mention of the same id format does not add new parameter meaning. It reinforces the correct usage but does not compensate for a coverage gap that does not exist.

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 opens with a clear statement: it fetches the full auction case content using the id returned by search. This verb+resource pairing is immediate and specific. It then explicitly contrasts this with realty_get_auction_case and realty_auction_history, making the tool's niche unmistakable.

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?

It gives explicit when-to-use guidance: this tool is for taking the exact search-returned id and getting document-like text. It names realty_get_auction_case for structured value handling and realty_auction_history for bid history, providing a clear decision rule among alternatives. The caveat about not fabricating rights relations also guides usage.

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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TDQS

A3.9/5.0
Disambiguation3/5

The set has very explicit cross-tool guidance and each tool is often given a specific 'role', but there are still many overlapping clusters: auction search vs auction list vs auction detail, regional price bands vs price stats vs rankings, and court auction rate vs public auction rate. The descriptions reduce misselection, but with 51 tools including pairs like `fetch` and `realty_get_auction_case`, confusion is still likely for an agent.

Naming Consistency4/5

Most tools follow a clean `realty_` prefix and use consistent snake_case noun-phrases or verb-noun patterns, e.g. `realty_search_auctions`, `realty_get_auction_case`, `realty_presale_cost`. The exceptions are the generic `fetch`, `search`, and `report_issue`, which break the uniform prefixed convention but are only a small minor deviation from an otherwise consistent naming system.

Tool Count1/5

51 tools far exceeds the recommended threshold, even for a deliberately broad real-estate area; it is effectively an extreme number for a single MCP server. The tool count becomes the hardest usability problem, since agents must handle many tightly related micro-tools instead of interacting with a smaller, more manageable surface.

Completeness5/5

The tool set covers an impressively complete range: auction and public-auction workflows, apartment and non-apartment market, presale/cheongyak processes, tax and loan rules, subscription scoring, redevelopment, demographics, supply, POI, and even a reporting and routing tool. Boundaries and unsupported cases are explicitly documented, so there are no major obvious dead ends.

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