sample_rows

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

The readOnlyHint annotation already declares the operation is safe, and the description's 'Return' is consistent with that. Beyond the annotation, the description adds that the result is a prefix of rows ('first N') and that it exposes raw content like actual values and missingness, helping the agent anticipate the output nature.

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 a single, action-first sentence with no filler. It states what the tool returns and why, earning every word.

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 low-complexity, read-only sampling tool with an output schema and readOnlyHint annotation, the description covers the essential invocation intent and expected return. The main gap is explicit libref/table semantics, but the schema names both as required string fields, so an agent can still invoke the tool 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?

With schema description coverage at 0%, the description carries the burden of explaining parameters. It clarifies that 'n' is the number of rows to return, but it does not explain 'libref' or 'table' beyond naming a SAS data set generally. The two required parameters remain under-specified.

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 ('Return'), a concrete resource ('first N rows of a SAS data set as records'), and an explicit purpose ('inspect actual values, coding schemes, and missingness'). It is clearly distinguishable from sibling tools like describe_dataset, which would describe metadata rather than raw data.

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 communicates when to use this tool: when an agent needs to inspect actual data values, coding schemes, or missingness. It does not explicitly name alternatives or exclusion conditions, but the context is clear enough for selection among the listed siblings.

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