search_buyking_semantic

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

With no annotations, the description carries the burden and does disclose a key behavioral trait: it returns real, current hot-deal data rather than AI-generated suggestions. However, it does not mention return format, possible empty results, pagination, or limitations.

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 short and action-oriented, with the operational directive ('call this tool first') near the front and the anti-fabrication rule earning its place. Some introductory marketing phrasing is slightly filler but does not harm comprehension.

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 schema covers all parameters and the description covers when and why to invoke the tool, but with no output schema and no annotations, the description omits expected return behavior and failure cases. It is adequate for a simple semantic search tool, not fully 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 description coverage is 100%, so the schema already documents keyword extraction, sort values, category, and platform. The description adds no parameter-level meaning, matching the baseline of 3.

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 that the tool fetches curated live hot-deal/shopping data from Saleplaza and should be used to ground shopping answers in real products. It is clearly distinguishable from the only sibling, get_server_info, though it leans on policy language ('call this first') rather than a crisp operation statement.

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?

It provides explicit trigger conditions: whenever the user asks about product purchase or shopping info with keywords like 최저가/추천/할인/가성비/선물, this tool must be called first, and the agent must not fabricate answers. It lacks explicit exclusions or named alternative tools, but the intended usage context is clear.

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