data_raw_search

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

With no annotations available, the description carries the behavioral transparency burden. It discloses two significant behaviors: it always returns minute-granularity raw rows, and it bypasses statistics tables—a signal that result sets may be large and unfiltered. It does not mention auth, rate limits, or side effects, but the core query behavior is transparent enough.

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?

Two short sentences front-load the main behavior (always minute-level raw data) and the key exclusion (no statistics tables). There is zero filler or redundancy, making it easy to parse quickly.

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 query tool with no output schema and no annotations, this description gives a solid high-level purpose but omits the output shape, column list, ordering, pagination, and performance implications of querying raw data over an arbitrary period. The schema's maxRows parameter partially compensates, but an agent still lacks a complete picture of what a response contains.

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 the parameters and their formats. The description adds only a general statement that the date range is unrestricted, but it doesn't deepen meaning about specific parameters like maxRows or date handling. The baseline of 3 is appropriate.

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 action ('조회한다' / query) and a specific resource ('분단위 원시 데이터' / minute-level raw data), and adds a key differentiator: it does not go through statistics tables. This makes it clearly distinct from statistical siblings like stat_daily_avg or data_recent, even without a tool title.

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 conveys clear usage context: use this tool when raw minute-level data is needed over any time period, and when aggregated/statistical processing is undesirable. It doesn't explicitly name alternative tools or state a when-not-to-use condition, but the contrast with statistics tables implicitly routes statistical queries elsewhere.

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