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korea-realty

by app.sallim

[유료] 단지 가격 예측 (XGBoost)

realty_predict_price
Read-onlyIdempotent

[유료] 단지의 다음 달 평균 매매가를 평형대별로 예측한다 (XGBoost v4_clean).

complex_name 또는 complex_key 중 하나는 필수. 동명 단지가 여러 지역에 있으면
먼저 realty_complex_report로 단지를 특정한 뒤 complex_key로 호출하라.
예측 지평은 1개월(익월) 고정 — 그 너머는 모델이 검증되지 않아 제공하지 않는다.
커버리지 밖은 정직하게 거절된다(지어내지 않음) — ①최근 3개월 내 월 거래 3건 미만이거나
②과거 거래 이력이 없는 신축 첫 달(모델이 지역·평형 평균을 토해 2~4배 틀린다, 실측).
예측이 없을 뿐 시세 데이터는 있으니 그때는 실거래 도구로 답하라.
응답 predictions[].caution이 있으면 반드시 함께 전달하라 — 예측 대상이 '익월에 거래된
매물들의 평균가'라, 시세가 그대로여도 거래 구성이 바뀌면 흔들린다(실측 16.3%가 ±10% 초과).
응답의 as_of_ym(기준월)·disclaimer(검증 MAPE)를 사용자 답변에 반드시 함께 전달하라 —
예측은 참고 지표이지 투자 보장이 아니다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthsNo(구버전 호환) 예측 지평은 1개월 고정 — 이 값은 무시되고 응답이 그 사실을 실토한다 (허용 범위 1~12)
complex_keyNo정확한 단지 키 — realty_complex_report가 돌려주는 complex_key
complex_nameNo단지명 (예: 반포자이)

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description goes further: discloses fixed 1-month horizon, explicitly admits out-of-coverage rejection rather than fabrication, provides failure-rate evidence (16.3% exceed ±10%), and transparently warns about response fields like caution, as_of_ym, and disclaimer. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Highly informative but tightly written; every sentence carries concrete operational or cautionary value. Key facts (essential parameter, sibling for disambiguation, coverage boundaries, pass-through of warnings) are front-loaded and no filler is present.

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?

Complete for a predictive tool with rich annotations and full schema coverage. The description covers required inputs, disambiguation workflow, model limitations, fallback behavior, and response-field obligations, leaving little an agent needs to infer.

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?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds value by explaining the months parameter is a legacy compatibility field that is ignored and that the API itself 'confesses' this in its response. But it adds no new syntax or format details beyond that; baseline 3 is appropriate.

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 states a specific verb ('predicts'), a precise resource ('next month's average sale price by pyeong type'), and the model (XGBoost). It clearly differentiates from siblings like realty_complex_pyeong_price by focusing on predictive output, not current market data.

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 states when to use (requires complex_name or complex_key), how to resolve ambiguity (use realty_complex_report first), and when not to use (coverage gaps, insufficient transaction data), while pointing to alternative tools for market data. This exceeds the minimum viable guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All korea-realty toolsInspect
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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.

Resources