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korea-realty

by app.sallim
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거시 지표 (기준금리·KOSPI·M2 시계열)

realty_macro_indicators
Read-onlyIdempotent

한국 기준금리·KOSPI·M2, 미 연준금리·S&P500 등 거시 지표의 월별 시계열을 조회한다. "금리가 집값에 어떤 영향?"류 배경 분석용.

⚠️ 계열마다 신선도가 다르다 — meta.series_as_of가 계열별 실제 최신 시점이다(예: 미
연준금리·S&P·코인은 최신인데 한국 기준금리·KOSPI는 2024-12 정지, ECOS 재수집 대기).
밀린 계열을 "지금 금리"로 인용하지 말고 반드시 그 계열의 시점을 함께 밝혀라.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthsNo최근 몇 개월치 (허용 범위 1~240)
indicatorsNo쉼표 구분 지표명: bok_base_rate(한국 기준금리 %), kospi(월말 종가), korea_m2(M2 평잔·원계열, 조원), fed_rate(미 연준금리 월평균 %), us_m2(미 M2 계절조정 $B), sp500, btc_usd, eth_usd(월말 종가). 비우면 전체. 목록에 없는 이름은 거절한다(조용히 버리지 않는다)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "title": "realty_macro_indicatorsDictOutput",
-  "type": "object"
-}New value: +null
  2. Changed1 schema field changed
    • changedInput schema / properties / indicators / description
      Previous value: -"쉼표 구분 지표명: bok_base_rate(한국 기준금리 %), kospi(월말 종가), korea_m2(M2 평잔·원계열, 조원), fed_rate(미 연준금리 월평균 %), sp500, btc_usd, eth_usd(월말 종가). 비우면 전체"New value: +"쉼표 구분 지표명: bok_base_rate(한국 기준금리 %), kospi(월말 종가), korea_m2(M2 평잔·원계열, 조원), fed_rate(미 연준금리 월평균 %), us_m2(미 M2 계절조정 $B), sp500, btc_usd, eth_usd(월말 종가). 비우면 전체. 목록에 없는 이름은 거절한다(조용히 버리지 않는다)"
  3. Changed1 schema field changed
    • changedInput schema / properties / months / description
      Previous value: -"최근 몇 개월치"New value: +"최근 몇 개월치 (허용 범위 1~240)"
  4. Changed1 schema field changed
    • changedInput schema / properties / indicators / description
      Previous value: -"쉼표 구분 지표명: bok_base_rate(한국 기준금리 %), kospi, korea_m2(조원), fed_rate(미 연준금리 %), sp500, btc_usd, eth_usd. 비우면 전체"New value: +"쉼표 구분 지표명: bok_base_rate(한국 기준금리 %), kospi(월말 종가), korea_m2(M2 평잔·원계열, 조원), fed_rate(미 연준금리 월평균 %), sp500, btc_usd, eth_usd(월말 종가). 비우면 전체"
  5. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, it discloses a critical behavioral trait: per-series data freshness differs, meta.series_as_of is the real latest timestamp, and some series (KR base rate, KOSPI) are frozen at 2024-12 pending ECOS re-harvest. It even instructs the agent to state each series' timestamp rather than quoting stale data as current — exactly the kind of context annotations cannot carry.

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 in the first sentence, usage context second, and the staleness caveat last as a warning block. It is appropriately sized, with only mild redundancy in restating specific indicator names already given in the schema.

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?

There is no output schema, so the description compensates by revealing the meta.series_as_of field and warning about per-series staleness — the main correctness trap for this tool. Combined with the 100%-covered parameter schema, an agent has what it needs, though it never notes pagination/volume limits for up to 240 months across eight series.

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 coverage is 100%: months and indicators are both fully described in the schema, including the comma-separated indicator names and the reject-unknown-names behavior. The description adds nothing parameter-specific beyond what the schema already provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (조회한다) and an enumerated resource (KR base rate, KOSPI, M2, US Fed rate, S&P500 monthly series), so an agent can immediately tell it returns macro time series rather than property-level data. It does not, however, name or differentiate itself from the potentially confusable sibling realty_market_signals.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a concrete usage context — background analysis for questions like 'how do interest rates affect home prices?' — which tells the agent when this tool fits. It offers no explicit exclusions or named alternative tools, so the routing guidance is clear but not complete.

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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