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.