Korean Agriculture Market Data

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

With no annotations provided, the description carries the burden. It discloses the return content (average, high, low, transaction volume) and the default behavior for the date parameter (empty string means today). However, it does not mention data freshness, market coverage, or error behavior, which could matter to an agent.

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 extremely concise: a one-sentence purpose followed by Args and Returns sections. Each line carries essential information with no redundancy, and the key action is front-loaded.

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?

Given the tool has only two parameters (one required) and an output schema exists, the description provides enough invocation details: parameter meanings, format, default, and a summary of the return structure. It lacks edge-case guidance (e.g., invalid keywords) but this is not critical for a straightforward compare tool.

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%, so the description must compensate. It does so effectively by explaining both parameters in Korean: product_keyword with concrete examples ('사과', '딸기', '배추') and date with format (YYYY-MM-DD) and default behavior. This adds substantial meaning beyond the bare schema.

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 specific verb ('비교합니다' = compares) and resource ('전국 도매시장 가격' = nationwide wholesale market prices) for a specific item, which clearly indicates what the tool does. It distinguishes the tool from siblings like get_market_list by focusing on price comparison, though it does not explicitly name alternatives.

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 use case is implied: use this when you need to compare nationwide wholesale market prices for a product keyword. However, there is no explicit guidance about when to use this tool versus siblings such as get_price_trend or search_product_price, and no exclusions are mentioned.

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

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.

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

With no annotations, the description carries the full burden. It clearly signals a read-only query operation ('조회합니다') and discloses the filter behavior for empty strings as nationwide. It does not discuss potential errors, rate limits, or data freshness, but for a simple lookup these are not critical gaps.

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, front-loaded with the primary purpose, and uses a clean Args/Returns structure. Every sentence adds useful information, and there is no filler or repetition of schema-only details.

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?

For a tool with one optional parameter and an existing output schema, the description is largely complete: it explains the parameter, default behavior, and return contents. It could slightly improve by noting that the returned codes are intended for use with other market-price tools, but nothing essential is missing for calling it correctly.

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?

The schema provides only a title and default for 'region', so schema description coverage is 0%. The description fully compensates by explaining the meaning of the parameter, providing concrete examples ('대전', '서울', '부산'), and specifying the behavior of an empty string as nationwide.

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 specific verb ('조회합니다' / retrieves) and a clear resource: the nationwide public wholesale market list and major category codes. This naturally distinguishes it from price- and auction-focused siblings such as get_realtime_auction and compare_market_prices.

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 does not explicitly state when to use this tool versus alternatives, nor does it mention using market codes as a prerequisite for price tools. The role is implied by the return value ('도매시장 코드 목록 및 부류 코드 목록'), but there is no explicit routing or exclusion guidance.

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

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

No annotations are provided, so the description carries the full burden of safety disclosure. The verb '조회' (query) and 'Returns: 일별 평균가격 추이' imply a non-destructive read operation, and the days parameter discloses a max of 30. However, it doesn't disclose pagination, failure behavior, or whether results are aggregated across all markets when market_code is empty. Adequate but not rich behavioral disclosure.

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?

Purpose is front-loaded in the first sentence, followed by a clean Args/Returns structure. Every line adds value — examples, defaults, and max constraints are compact. Only minor waste: the '오늘' date-relative phrasing could be more precise, but overall it's lean and scannable.

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?

For a 3-parameter query tool with an output schema covering the return shape, the description covers parameter semantics and default behavior. The return value is summarized as '일별 평균가격 추이', which is sufficient given the output schema. It's complete enough for an agent to invoke it correctly.

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%, and the description compensates well: it explains product_keyword as an item keyword with concrete examples ('사과', '딸기'), market_code as a wholesale market code with empty-string-means-nationwide semantics, and days as the inquiry period with default 7 and max 30. This goes meaningfully beyond the bare schema names, which only have titles.

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?

States a specific verb (조회/query) and resource (품목의 최근 N일간 가격 추이 = item price trend over N days). The focus on time-series price trends distinguishes it from siblings like compare_market_prices and get_realtime_auction, though it doesn't explicitly name them. The '일별 평균가격 추이' detail clarifies the output is daily averages, making the core function unambiguous.

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?

No guidance on when to use this tool versus the five siblings. It never mentions alternatives, exclusions, or prerequisites — an agent must infer from names that get_price_trend is for trends while compare_market_prices or search_product_price serve other needs. No explicit when/when-not guidance is provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses that this is a query operation ('조회') and mentions the real-time nature and data volume, but does not cover authentication, rate limits, data freshness, or error handling. The parameter defaults and empty-string behavior are documented, which adds some transparency, but the overall behavioral context remains basic.

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 well-organized with an Args and Returns section, front-loading the purpose before parameter details. It is not overly long, though some verbosity exists (e.g., repeating the 63,000+ data point). Overall, it is structured and efficient, with each sentence contributing to understanding.

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?

Given the tool has an output schema (per context), the description does not need to detail the return structure; it still lists the return fields. All four parameters are explained with defaults and examples. Minor aspects like error handling or data freshness are not covered, but for a read-only query tool with an output schema, the description is sufficiently complete.

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?

The schema has 0% coverage, but the description fully compensates by explaining every parameter with concrete examples (market_code: '250003'=대전노은, category_code: '06'=과실류), formats (date as YYYY-MM-DD), and semantics for empty strings (nationwide/all). It also specifies num_results default and max. This provides rich meaning beyond the bare schema, enabling 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 clearly states it retrieves real-time auction data from nationwide wholesale markets using the specific verb '조회' with a clear resource ('실시간 경매 현황'). It also provides scale (63,000+ data). While it doesn't explicitly name sibling tools, the real-time auction focus distinguishes it from price trends, summaries, and comparisons, making the purpose unambiguous.

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 implies usage context through the real-time auction nature and parameter scoping (empty strings for nationwide/all), but it does not explicitly state when to use this tool versus alternatives like get_price_trend or get_auction_summary. There is no guidance on exclusions or when to prefer a sibling tool, so usage is left to inference.

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

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

With no annotations, the description carries the disclosure burden. It does add useful behavior context: empty market_code/date semantics and num_results max of 1000. However, it does not mention read-only safety, authentication requirements, rate limits, or error behavior. 'Search' implies a read operation, but deeper behavioral transparency is limited.

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 Args and Returns sections. Every sentence adds value, and the purpose is front-loaded in the first line. No redundant or ambiguous content.

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?

For a straightforward search tool with an output schema, the description sufficiently covers purpose, parameters, and return summary. What's missing is explicit routing among sibling tools and any caveats about data coverage or latency, but overall it is nearly complete.

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 coverage is 0%, but the description fully compensates by explaining every parameter in Korean: product_keyword with examples, market_code default meaning, date format and default, and num_results default/max. This adds substantial meaning beyond the raw schema.

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 specific verb and resource: '검색합니다' (searches) '전국 도매시장 경매 가격' (nationwide wholesale market auction prices) by product keyword. This clearly identifies the tool's function, though it does not explicitly differentiate from sibling tools like get_realtime_auction or get_price_trend.

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?

Usage is only implied through the description and parameter defaults (e.g., empty market_code means nationwide, empty date means today). There is no explicit guidance on when to use this tool versus alternatives such as compare_market_prices, get_price_trend, or get_auction_summary.

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