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StockLens

by Johnhyeon
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Remote

get_indicators

Read-onlyIdempotent

Get technical indicator judgments for stocks: moving averages, RSI, MACD, Bollinger Bands, Stochastic, volume, position. Use them for numeric screening and condition filters without parsing OHLCV.

Instructions

기술지표 — 이평선·RSI·MACD·볼린저·스토캐스틱 등 종합 판정 (JSON).

스크리닝·조건 필터·상태 판정 등 숫자 비교가 필요할 때만 호출. 차트 시각화용 아님(시각화는 get_chart). OHLCV 대신 판정 결과만 반환해 토큰 절약. 반환값의 라벨 필드(phase_label, type_label, position 등)는 그대로 인용할 것.

키 이름이 곧 정의입니다 — 임의로 바꿔 읽지 마세요: volume.latest(+latest_date) 마지막 봉의 거래량. '오늘'이 아님 volume.avg_20b / ratio_vs_avg_20b 20봉(거래일) 평균 대비 volume.trade_value_est_krw 종가×거래량 추산. 실제 거래대금과 다름 volume.volume_rank_252b 252봉 중 순위, 1이 최다 position.bars_since_high/low 달력일이 아니라 봉 개수. 달력일이 필요하면 high_date/low_date로 직접 계산하세요. position.high_52w/low_52w 봉이 1년치(일 252·주 52·월 12)가 안 되면 null. 그때 조회 구간 고저는 lookback_high/low 에 있습니다 주봉·월봉의 크로스 경과는 days_ago 가 아니라 bars_ago(봉 개수) _meta.data_basis가 in_progress_bar면 마지막 봉이 미마감이라 이 판정들은 장 마감 시 달라질 수 있습니다.

Args: code: 종목코드 (예: "005930") days: 조회 일수 (기본 260, 30~500). 구조 분석 지표는 500+ 권장. include: 지표 키. 기본 ["ma", "ma_phase", "volume", "candle"]. 스냅샷: ma ma_phase ma_slope ma_cross rsi macd bollinger stochastic obv volume position candle 구조: support_resistance volume_profile price_channel timeframe: "day"/"week"/"month" (분봉 미지원) params: 비표준 파라미터 오버라이드(사용자 명시 요청 시만). 예: {"rsi":{"period":21}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
daysNo
paramsNo
includeNo
timeframeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed10 schema fields changedv1.1.3
    • removedInput schema / properties / days / default
      Removed value: -260
    • removedInput schema / properties / include / anyOf
      Removed value: -[
-  {
-    "items": {
-      "type": "string"
-    },
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]
    • removedInput schema / properties / include / default
      Removed value: -null
    • addedInput schema / properties / include / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / include / type
      Added value: +"array"
    • addedInput schema / properties / params / additionalProperties
      Added value: +true
    • removedInput schema / properties / params / anyOf
      Removed value: -[
-  {
-    "additionalProperties": true,
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]
    • removedInput schema / properties / params / default
      Removed value: -null
    • addedInput schema / properties / params / type
      Added value: +"object"
    • removedInput schema / properties / timeframe / default
      Removed value: -"day"
  2. First observedv0.4.0

TDQS

A5/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, but the description adds rich behavioral context: `volume.latest` refers to the last bar not today, `bars_ago` vs `days_ago` distinction, null handling for insufficient history, `trade_value_est_krw` as an estimate, and the `in_progress_bar` caveat that judgments may change before market close. These go well beyond the 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?

Although lengthy, every sentence adds unique value: purpose, usage, key field definitions, parameter details, and data caveats are all present without redundancy. The structure is logical, starting with the core purpose, then usage, then field semantics, then parameters. No wasted words.

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?

The tool is complex (many indicators, parameter variations, and edge cases), yet the description covers all critical aspects: when to use, what indicators are included, key field meanings, parameter ranges and defaults, and the incomplete-bar caveat. With an output schema present, the return format is handled separately, so nothing an agent needs to call correctly is missing.

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?

Schema description coverage is 0%, so the description carries the full burden. It provides concrete examples (code '005930'), default and range for days (260, 30–500), the full list of include keys split into snapshot and structure categories, timeframe options, and an example of params override. This compensates entirely for the schema's lack of descriptions.

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 clearly states it provides technical indicator judgments (이평선·RSI·MACD·볼린저·스토캐스틱 등) and explicitly differentiates it from chart visualization (get_chart) and raw OHLCV data. It specifies the exact use case: screening, condition filtering, and status judgment when numeric comparisons are needed.

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?

The description gives explicit when-to-use guidance ('숫자 비교가 필요할 때만 호출') and when-not-to-use ('차트 시각화용 아님'), naming the alternative tool (get_chart). It also explains the token-saving benefit, leaving no ambiguity about selection.

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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