도매시장 경매 데이터의 부류별/시장별 통계를 요약합니다.
Args:
market_code: 도매시장 코드. 빈 문자열이면 전국.
date: 정산일 (YYYY-MM-DD). 빈 문자열이면 오늘.
Returns:
부류별 평균가, 거래건수, 시장별 거래 현황 요약
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| market_code | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose key behaviors: empty market_code means nationwide, empty date means today, and it returns average price, transaction count, and market status summary. However, it does not state whether the operation is read-only or any side effects, though the 'get' prefix implies read-only. It adds useful defaults and return format details, but lacks explicit safety/permission disclaimers.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with an Args and Returns section. It is not verbose, and each sentence adds value. The Korean text is efficient, though it could be slightly more compact. It earns a 4 for being appropriately organized without unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is fairly complete for a simple tool with two optional parameters. It explains the defaults, the return summary, and the parameters. It does not specify the format of market_code (e.g., numeric code length), which could be a minor gap. Given the output schema exists and the tool is straightforward, the description covers most contextual needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no parameter explanations. The description compensates fully: it explains market_code as 'wholesale market code; empty string means nationwide' and date as 'settlement date (YYYY-MM-DD); empty string means today'. This gives both semantic meaning and default behavior, which is essential for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb and resource: 'Summarizes statistics by category/market of wholesale market auction data' ('도매시장 경매 데이터의 부류별/시장별 통계를 요약합니다'). It clearly identifies the tool's function and the resource (auction data). It is distinct from siblings like compare_market_prices and get_price_trend, but does not explicitly name them for differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention conditions like 'for real-time data use get_realtime_auction' or 'for comparisons use compare_market_prices'. There are no usage recommendations or exclusions.
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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