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korea-realty

by app.sallim

단지 학군·교통 점수

realty_location_scores
Read-onlyIdempotent

단지의 학군(v5)·교통(지하철·버스) 점수를 조회한다 — "이 아파트 학군 어때? 역세권이야?" 담당. complex_key/complex_name이면 단지 점수, region만 주면 지역 집계+축별 상위 5 단지.

학군 점수는 학원가 강도+군집 보정의 **스크리닝 지표**다 — 학원가 데이터가 시 단위라
도심 외곽 면/읍이 다소 높게 나올 수 있고, 학교 배정·수준 판단이 아니다(그렇게 팔지 말 것).
subway_distance_m는 최근접역 직선거리(도보 거리 아님). 점수 미산정 단지는 not_found가
정직한 답이다 — 지어내지 말고 realty_search_complexes로 단지 실존부터 확인하라.
돌려주는 complex_key는 공백 1칸으로 정규화돼 있어 다른 도구에 그대로 넣을 수 있다.
"역세권이야?"는 [유료] realty_poi_nearby와 겹친다 — 점수·스크리닝은 이쪽(무료),
주변 시설의 실제 목록·거리가 필요하면 poi_nearby.
**realty_region_rankings(metric=school|transit)의 점수와 섞지 마라** — 이름은 같지만
원장·산식이 다른 판본이라(응답 score_lineage) 같은 단지에서도 값이 갈린다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNo지역명 — 단독이면 지역 집계+상위 단지, complex_name과 함께면 검색 범위
complex_keyNo정확한 단지 키 — realty_search_complexes가 돌려주는 complex_key
complex_nameNo단지명 일부 (부분일치)

TDQS

A5/5.0
Behavior5/5

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

The annotations already establish read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable caveats beyond those annotations: the score is a screening indicator, city-level hagwon data may skew outer areas, subway_distance_m is straight-line not walking distance, not_found is an honest response, and returned complex_key is normalized with a single space. 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?

The description is long but information-dense; every sentence carries a distinct caveat, routing rule, or normalization behavior. Key facts are front-loaded, and bolding makes the warnings scannable. There is no filler.

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?

Given no output schema, the description still covers expected response shapes (regional aggregation, top-5, complex scores), the not_found behavior, the score_lineage lineage marker, and how to reuse the returned complex_key. This is sufficient for an agent to select and invoke 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?

Although schema coverage is 100%, the description adds semantically important detail: complex_key must be exact and comes from realty_search_complexes, complex_name is partial match, and region alone changes the result into a regional aggregate plus top-5 list. It also notes the returned complex_key is normalized for direct reuse in other tools.

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 and resource: it queries a complex's school-district (v5) and transit (subway/bus) scores. It also differentiates itself from multiple siblings by clarifying what it is not, such as not being a school-assignment judgment and not being the realty_region_rankings scoring version.

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 usage rules: complex_key/complex_name yields complex scores, while region alone yields regional aggregation plus top-5 complexes. It tells the agent to verify existence via realty_search_complexes when not_found, and routes actual POI lists/distances to realty_poi_nearby. It also warns against mixing with realty_region_rankings.

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.

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