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StockLens

by Johnhyeon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_event_reactions

Read-onlyIdempotent

Compare a stock's price and volume response across multiple disclosure dates in one call, revealing whether reactions to news are improving or fading.

Instructions

공시반응이력 — 최근 공시들에 이 종목이 어떻게 반응해 왔는지 한 번에.

"이 종목은 호재에 잘 오르나", "공시 나오면 어떻게 움직였나", "최근 반응이 예전보다 약해졌나" 같은 질문에 사용합니다.

get_event_reaction은 날짜 하나만 봅니다. 공시 8건을 보려면 8번 불러야 하고 응답도 8배가 됩니다. 이 도구는 일봉·수급을 한 번만 받아 여러 공시일을 한꺼번에 계산합니다.

⚠️ 반응이 좋았다고 앞으로도 그러리라는 뜻이 아닙니다. 과거 기록일 뿐입니다. ⚠️ 네이버 공시 목록에는 '가격제한폭 확대요건 도달' 같은 시장 안내도 섞입니다. 제목을 보고 실제 기업 공시인지 구분하세요.

Args: code: 종목코드 6자리 max_events: 볼 공시 개수 (기본 8, 최대 15). 같은 날 여러 건이면 하나로 묶습니다. before: 기준일 전 비교 거래일 수 (기본 5) after: 기준일 후 비교 거래일 수 (기본 10, 최대 60) include_types: 이 유형만 봅니다. 유형: 실적/계약/자금조달/지배구조/행정/IR/기타. 예: ["실적"] 이면 가격제한폭 확대·공매도 과열 같은 행정 안내가 빠집니다. exclude_types: 이 유형을 뺍니다 (include_types 와 함께 쓰면 include 적용 후 제외).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
afterNo
beforeNo
max_eventsNo
exclude_typesNo
include_typesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed11 schema fields changedv1.1.3
    • removedInput schema / properties / after / default
      Removed value: -10
    • removedInput schema / properties / before / default
      Removed value: -5
    • removedInput schema / properties / exclude_types / anyOf
      Removed value: -[
-  {
-    "items": {
-      "type": "string"
-    },
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]
    • removedInput schema / properties / exclude_types / default
      Removed value: -null
    • addedInput schema / properties / exclude_types / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / exclude_types / type
      Added value: +"array"
    • removedInput schema / properties / include_types / anyOf
      Removed value: -[
-  {
-    "items": {
-      "type": "string"
-    },
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]
    • removedInput schema / properties / include_types / default
      Removed value: -null
    • addedInput schema / properties / include_types / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / include_types / type
      Added value: +"array"
    • removedInput schema / properties / max_events / default
      Removed value: -8
  2. Addedv1.0.1

TDQS

A5/5.0
Behavior5/5

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

Annotations already mark readOnly/idempotent/non-destructive; description adds behavioral caveats: past reaction does not predict future, Naver list contains market notices, same-day events are grouped, and only one candle/flow fetch is used. 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?

Each sentence earns its place: purpose, differentiation, warnings, then Arg list. Front-loaded with the one-line value proposition, and the caveats are kept as brief warnings rather than digressions.

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 6-param batch tool with an output schema, the description covers all invocation semantics: defaults, limits, type filtering, and interpretation warnings. The output schema handles return shape, so nothing necessary for correct use 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 description carries full parameter burden. It explains code format, max_events default/max and grouping, before/after defaults and caps, include_types categories and example, and exclude_types precedence after include. This is far beyond schema field names.

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?

Description opens with '공시반응이력 — 최근 공시들에 이 종목이 어떻게 반응해 왔는지 한 번에' and contrasts with single-date get_event_reaction, making the batch-scope purpose explicit. It states a specific verb+resource and differentiates from the sibling tool.

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?

Gives concrete example questions and an explicit comparison: get_event_reaction looks at one date, requiring 8 calls for 8 disclosures, while this tool computes multiple event dates in one call. It also warns about non-corporate market notices and recommends include_types, which is actionable when-to-use guidance.

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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