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

With no annotations, the description carries the full behavioral burden. It does disclose the core selection logic (match any value in a column) and ordering (in the order given), which is helpful. However, it does not specify output format, exact-match semantics, case sensitivity, or behavior when no rows match.

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, front-loaded sentence that conveys purpose, scope, ordering, and use case with no wasted words. Every clause adds information.

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 simple two-parameter read-style tool with no output schema, the description is largely complete: it explains what rows are returned, in what order, and when to use it. Minor omissions around return representation and edge cases keep it from a 5.

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 does: 'column' is clearly the field to match on, and 'values' are the allowed match values with output order tied to their given order. This adds real meaning beyond the bare schema types.

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 identifies the resource (rows of the Pickpathly dataset) and the selection behavior (rows whose column matches any of the given values, in the order given). It is distinguishable from siblings like dataset_search or dataset_row, though it lacks an explicit action verb like 'returns' or 'fetches'.

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 'for "X vs Y" questions' provides a clear usage context: use this when comparing specific values side by side. It does not explicitly name alternative tools or state when not to use it, but the intended scenario is clear enough.

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