Korean Agriculture Market Data

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

No annotations are present, so the description carries the burden. It states the tool returns a price comparison with average, high, low, and volume, implying a read-only operation. However, it does not explicitly state safety (e.g., no side effects) or mention rate limits, authentication, or behavior on missing data. The description is adequate but not rich.

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: one sentence for purpose, then clear Args and Returns sections. No extraneous information; every sentence adds value. It is front-loaded with the main action.

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 2 parameters (1 required), no enums, and an output schema (not shown but described), the description covers the tool's basic functionality. It mentions return fields (average, high, low, volume) but lacks guidance on error handling or edge cases (e.g., no data for keyword). It is fairly complete but could benefit from linking to related tools.

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 provides examples for product_keyword (e.g., '사과') and format for date (YYYY-MM-DD, default today). This adds useful context beyond the bare schema, though it could be more specific (e.g., list of valid markets).

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 the tool compares wholesale market prices nationwide for a specific item (verb 'compares' + resource 'wholesale market prices'). It lacks explicit differentiation from sibling tools, but the mention of 'nationwide' implies comparison across markets, distinguishing it from tools like get_price_trend (trend over time) or get_market_list (list markets).

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 usage guidelines are provided. The description does not indicate when to use this tool versus siblings (e.g., get_price_trend for trends, search_product_price for searching). There is no mention of preconditions, alternatives, or cases where this tool is inappropriate.

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 exist, so the description carries the full burden. It states returns summary statistics but does not disclose if the tool is read-only, idempotent, or requires authentication. The behavioral profile is incomplete.

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 fairly concise with a main sentence and structured Args/Returns sections. The Args section could be slightly tighter, but overall it is well-organized and efficient.

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's simplicity (2 optional params, output schema exists), the description covers parameter semantics and return format adequately. It does not mention limitations or prerequisites but is sufficient for basic use.

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?

With 0% schema coverage, the description fully compensates by explaining both parameters: market_code (empty = nationwide) and date (YYYY-MM-DD, empty = today). This adds essential meaning beyond the schema defaults.

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 summarizes wholesale market auction data by category and market statistics. It distinguishes from siblings like get_realtime_auction (real-time focus) and compare_market_prices (comparison), though it does not explicitly contrast.

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 provide guidance on when to use this tool vs alternatives. It lacks usage context, prerequisites, or exclusions, leaving the agent to infer from the purpose alone.

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 provided, so description carries full burden. It correctly indicates a read-only retrieval, but lacks details on return structure, any constraints, or required authentication.

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?

Very concise with a clear structure: one-line purpose, Args block, Returns block. Every sentence adds value.

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?

With an output schema present, the description adequately explains what the tool returns (lists of codes). The single optional parameter is well-described. No missing pieces for this simple 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?

The only parameter 'region' is explained with examples and behavior for empty string. Schema coverage is 0%, so the description adds essential context beyond the 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 clearly states the verb '조회' (retrieve) and the resource: national public wholesale market list and category codes. It distinguishes itself from sibling tools (price, auction, trend) by focusing on static metadata.

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 explicit guidance on when to use this versus alternatives. The description implies it provides codes for other tools, but does not state exclusions or prerequisites.

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 must disclose behavioral traits. It implies a read-only query operation but does not explicitly state safety, rate limits, or permissions. However, the return description ('daily average price trend') adds some transparency.

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. It wastes no words, but could be slightly more front-loaded. Still efficient and easy to parse.

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 simple query tool with an output schema, the description covers all necessary aspects: purpose, parameters, and return type. It lacks edge cases or error handling, but that is acceptable given the tool's simplicity.

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 description adds substantial meaning beyond the schema: explains product_keyword with examples, market_code as empty string for nationwide, and days with default and maximum. Since schema description coverage is 0%, this fully compensates.

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 price trends for an item over N days, with specific parameters for product keyword, market code, and days. This distinguishes it from sibling tools like compare_market_prices, get_auction_summary, and search_product_price, which have different purposes.

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 context like trend analysis or comparisons, leaving the agent without explicit direction for tool selection.

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 full burden. It mentions '실시간' (real-time) but doesn't explain latency, rate limits, or whether data is read-only. The Args section gives parameter limits (e.g., max 1000 results), which is helpful, but broader behavioral context is missing.

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-structured with a brief summary followed by Args and Returns. It is front-loaded with the core purpose. While slightly verbose, it is not overly long and every sentence contributes useful information.

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 presence of an output schema, the description need not detail return format. However, it lacks usage guidelines and behavioral transparency. Parameter details are strong, but overall completeness is adequate but not exceptional for a tool with 4 parameters and no annotations.

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%, so description must compensate. The Args section provides detailed explanations for all four parameters: examples for market_code and category_code, date format, and default/max for num_results. This adds essential meaning beyond the schema's bare titles and defaults.

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 status from nationwide wholesale markets. The verb '조회합니다' and resource specification are precise. However, it does not explicitly differentiate from sibling tools like get_auction_summary or compare_market_prices, missing an opportunity to clarify unique value.

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 is provided on when to use this tool versus siblings. The description implies real-time data access but offers no explicit conditions, exclusions, or alternatives. Users are left to infer, which may lead to incorrect tool selection.

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 the return format (item price info + market summary) but does not mention auth requirements, rate limits, or whether the operation is read-only. The behavior is partially transparent.

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 highly concise, front-loaded with the purpose, and organized with clear Args/Returns sections. Every sentence adds value, and the structure is easy to parse.

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 4 parameters and an output schema exists, the description adequately covers input semantics. It mentions the return summary but not error cases or edge behavior. However, the output schema mitigates the need for full return detail, so it is largely 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 description coverage is 0%, but the description adds significant meaning: it explains product_keyword with examples, market_code as empty for nationwide search, date as settlement date in YYYY-MM-DD, and num_results with defaults. This goes 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 explicitly states it searches auction prices across domestic wholesale markets by product keyword. It uses a specific verb ('search') and resource ('auction prices'), and the sibling tools (e.g., compare_market_prices, get_price_trend) focus on different operations, so this tool is clearly distinct.

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 by specifying input arguments, but does not explicitly state when to use this tool versus siblings, nor does it provide conditions for when not to use it. No mention of alternative tools or exclusions is given.

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