sample_rows

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

Annotations include readOnlyHint=true, which already covers safety. The description adds behavioral context such as the effect of columns on response size and determinism with seed. It does not contradict annotations, and the added details go beyond what annotations provide.

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?

Three concise sentences: purpose, usage context, and key parameter tips. Front-loaded and every sentence contributes useful information with no fluff.

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?

Despite having 8 parameters, the schema covers them thoroughly and the description provides the essential rationale for usage and key parameter behaviors. With no output schema required, the description is complete for understanding when and how to use the 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?

Schema description coverage is 100%, so baseline is 3. The description enhances parameter meaning by explaining why columns reduce response size and how seed enables reproducible sampling, adding value beyond the schema's basic descriptions.

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 returns a sample of rows, which is specific and distinct in purpose. However, it does not explicitly differentiate from sibling tools like get_rows, which could also return rows, so it lacks direct sibling differentiation.

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 a clear usage context: 'Useful for understanding data shape without prior knowledge.' It also gives practical parameter tips (use columns for wide tables, seed for reproducibility) but does not mention when to use alternatives or exclusions, so it lacks explicit when-not-to-use guidance.

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