Korean Stock Market Data

compare_stocks

여러 종목의 시세를 비교합니다.

Args:
    stock_names: 비교할 종목명 (쉼표 구분, 예: "삼성전자,SK하이닉스,NAVER")
    date: 조회일 (YYYY-MM-DD). 빈 문자열이면 최근 영업일.

Returns:
    종목별 시세 비교 (종가, 등락률, 거래량, 시가총액)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "stock_names": "<stock_names>"
    },
    "name": "compare_stocks"
  }
}

Input Schema

Name	Required	Description	Default
`date`	No
`stock_names`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedMar 9, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden. It transparently explains the read-style comparison behavior, the meaning of an empty date (most recent business day), and the output fields (close, change rate, volume, market cap). It does not explicitly state that it is non-mutating, but the comparison semantics make that clear.

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 compact and well-structured: one purpose sentence, then Args and Returns sections. Every line adds useful information, and the content is front-loaded with the core purpose before parameter details.

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 two-parameter read-only lookup tool, the description covers everything an agent needs: purpose, parameter format, date defaults, and return semantics. The presence of an output schema also reduces the need to explain return structure in more detail.

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%, and the description fully compensates. It explains both parameters: stock_names should be comma-separated with examples, and date uses YYYY-MM-DD format with empty-string fallback behavior. This goes well beyond the bare schema fields.

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 opens with a specific verb and resource: '여러 종목의 시세를 비교합니다' (compares market prices of multiple stocks). It also lists the returned metrics, making the tool's job unambiguous and distinguishing it from single-stock siblings like get_stock_price.

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

Usage Guidelines3/5

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

The use case is clearly implied by '여러 종목' (multiple stocks), so an agent can infer this is for comparing several tickers at once. However, it never explicitly says when not to use it or names alternatives such as get_stock_price or get_price_trend, leaving the routing decision to the agent's inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Korean Stock Market Data tools Inspect

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