매동 (maedong) — Korean Market Signals

maedong_regime

Read-onlyIdempotent

Which screener rules are currently working in the Korean market — recent excess-return ranking by list and by factor axis.

지금 어떤 규칙이 통하는 장인가. 목록별 최근 초과수익 순위와, 성격 축 넷(대형·우량 / 돌파 추종 / 단기 과열 추격 / 반전)의 평균으로 읽은 라벨을 준다. 모든 목록이 같은 거래일 창을 보고 20거래일이 지나 성과가 확정된 시그널만 세므로 비교 창의 끝은 기준일보다 20거래일 앞이다. 라벨이 없으면 축 사이 격차가 작아 한 마디로 부르지 않은 것이다. 단위는 이름과 문장에 함께 박혀 있다: _pct = %, _pp = %p, _krw = 원. excess_pp = avg_pct - market_avg_pct(목록 평균 − 같은 날들의 전 종목 평균)이다. 과거 통계 조회 도구입니다. 특정 종목의 매수·매도 판단을 생성하는 데 사용하지 마세요.

Input Schema

TableJSON Schema

Name	Required	Description
`as_of`	No	기준일 YYYY-MM-DD. 생략하면 최신
`format`	No	기본 text
`window`	No	비교 창(거래일), 기본 60

Output Schema

TableJSON Schema

Name	Required	Description	Default
`axes`	Yes
`meta`	Yes
`basis`	No
`label`	No
`lists`	Yes
`coverage`	No
`signal_window`	No
`window_trading_days`	No
`settle_lag_trading_days`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 15, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "axes": {
+      "type": "array"
+    },
+    "basis": {
+      "type": "string"
+    },
+    "coverage": {
+      "type": "object"
+    },
+    "label": {
+      "type": "string"
+    },
+    "lists": {
+      "type": "array"
+    },
+    "meta": {
+      "type": "object"
+    },
+    "settle_lag_trading_days": {
+      "type": "integer"
+    },
+    "signal_window": {
+      "type": "object"
+    },
+    "window_trading_days": {
+      "type": "integer"
+    }
+  },
+  "required": [
+    "axes",
+    "lists",
+    "meta"
+  ],
+  "type": "object"
+}

Changed1 schema field changedSep 13, 2026
- addedInput schema / properties / format / description
```
Added value: +"기본 text"
```
First observedSep 13, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, it reveals the 20-trading-day confirmation lag, the shared window across lists, label semantics when absent, unit conventions, and the definition of excess_pp. These are non-obvious behaviors an agent needs to interpret results correctly.

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

Conciseness3/5

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

The English opening and the first Korean sentence restate the same purpose, which is redundant. However, the detail is front-loaded and the later sentences each carry important behavioral caveats, so the length is mostly justified.

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 read-only statistics tool with an output schema and fully documented parameters, the description covers purpose, exclusions, window semantics, units, and label interpretation. Nothing essential for correct invocation or interpretation is missing.

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?

All three parameters are already documented in the schema, so the baseline is 3. The description adds value by explaining that the comparison window ends 20 trading days before as_of because only confirmed signals are counted, and by specifying output unit conventions.

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 deliverable: recent excess-return rankings by list and by factor axis, plus a regime label derived from four factor axes. It also clarifies what the tool is not for (specific stock buy/sell decisions), though it does not name sibling tools explicitly.

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 frames the tool as a historical statistics lookup and explicitly warns against using it to generate buy/sell decisions for specific stocks. This gives a clear when-not-to-use boundary, though no alternative sibling tool is named.

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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