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

With no annotations, the description is the only source of behavioral disclosure. It usefully reveals equality-based matching over the 'any of' values and that output order follows the input order, but it does not describe the output presentation, exact-match/case sensitivity, duplicate handling, or empty-result behavior.

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?

One sentence contains the full selection semantics and the use case with no filler. It is front-loaded with the core behavior and ends with the purpose, making it easy to scan and act on.

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 tool is simple and the core selection behavior is clear, but because there are no annotations and no output schema, the description should say more about what the returned rows look like and whether a value with zero matches is silently omitted. The missing output-format detail is the main gap.

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?

The schema has no descriptions for column or values, but the description connects column to the dataset column being filtered and values to the accepted row values. It also clarifies that the values order determines row order, adding useful semantics beyond the bare string/array schema.

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 defines the result set: rows from the Clipboardo dataset matching the given values in a specified column, in the provided order. The 'X vs Y' phrasing signals comparison use and distinguishes it from dataset_row and dataset_search, though it relies on the title for the explicit verb rather than stating 'returns' or 'lists'.

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 'for X vs Y questions' clause gives an explicit when-to-use context: whenever an agent needs to compare specific rows or entities. It does not name alternatives or state when not to use it, but the comparison framing is enough to route the agent away from the single-row, stats, and search tools.

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