sample_rows

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It does state 'Read' (implying non-mutating) and mentions the default limit of 10 rows, which is useful. However, it does not explain whether the sample is random or sequential, how it handles large tables, or what the return format looks like – leaving gaps in transparency.

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 clear sentence that front-loads the action and purpose, then adds the default limit. Every word earns its place; there is no fluff or repetition.

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's simplicity and absence of an output schema, the description covers the essential purpose and default behavior. It does not explain return structure or sampling strategy, but the name and description together provide sufficient context for an agent to understand what the tool does. Sibling tools are contextually related, but the description doesn't explicitly guide selection among them, slightly reducing completeness.

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 0%, so the description must compensate. It adds meaning to 'limit' by stating a default of 10 rows, but it doesn't mention min/max bounds or that it's optional. For 'database' and 'table,' the parameter names are self-explanatory, so this partial compensation is adequate but not comprehensive.

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's function: 'Read a small sample of rows from a table to understand its shape.' This uses a specific verb (read) and resource (sample of rows from a table), and it distinguishes from sibling tools like 'describe_table' (schema) and 'query' (full query) by emphasizing a small sample for shape understanding.

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 phrase 'to understand its shape' implies the tool is for quick, exploratory data checks, but it does not explicitly name alternatives or state when not to use it. There's no mention of 'use describe_table for schema' or 'use query for full results,' so guidance is merely implied rather than explicit.

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