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

It discloses key behaviors: matching rows where the column equals any of the given values, and returning them in the order of the values array. However, with no annotations provided, it omits the return shape, handling of no-match values, and case sensitivity, so behavioral disclosure is only partial.

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 sentence with no filler. It front-loads the dataset name and core behavior, and the 'X vs Y' note adds genuine usage context without wasting words.

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-only tool with no output schema, the description covers the dataset, matching behavior, ordering, and intended use case. Minor gaps remain around the exact row format and error behavior, but nothing essential is missing for an agent to call it 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?

Schema description coverage is 0%, so the description must compensate. It maps both parameters ('column' and 'given values') to their roles in filtering and adds the important ordering semantics of the values array, but it does not address edge cases like duplicate values, empty strings, or what happens when no rows match.

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 what the tool does: it returns rows of the Footfally dataset filtered by a column whose values appear in the given list, preserving the order of that list. This distinguishes it from siblings like dataset_search or dataset_row, though it lacks an explicit verb such as 'returns' or 'selects'.

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 gives a clear use case ('for "X vs Y" questions') that signals when this tool is appropriate. It does not explicitly name alternative tools or state when not to use it, so some routing is left to inference.

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