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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal two important behaviors: rows are filtered by equality on the column and are returned in the order given by the values array. However, it does not mention output format, exact-match semantics, case sensitivity, or pagination, which would make behavior more transparent.

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 compact sentence with no filler. It front-loads the core filtering behavior and ends with the use-case context, so every word earns its place.

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 filtering tool, the description covers the essential details: what is filtered, how values map to rows, and the ordering behavior. It would be more complete if it explicitly described the output shape or exact-match constraints, but the title and description together give enough context for correct invocation.

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, and it does. It explains that 'column' is the field to match and 'values' are the values to match, and it adds the crucial semantic that output row order follows the order of the values array. This goes beyond the bare schema names and gives the agent actionable meaning.

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 what the tool returns: rows of the ProbeLedger dataset filtered by a column matching given values, with order preserved. The 'X vs Y' phrasing signals the comparison use case and helps distinguish it from siblings like dataset_search and dataset_row, though it does not explicitly name those alternatives.

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 context ('for X vs Y questions') that implies when this tool is appropriate, but it does not explicitly state when to use it instead of sibling tools like dataset_search or dataset_row. There are no exclusion criteria or alternative routing.

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