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

With no annotations, the description carries the behavioral disclosure burden. It usefully discloses row selection, equality filtering, and value-order output ordering. However, it leaves some behavior implicit, such as exact-match semantics, case sensitivity, handling of duplicates, and the exact shape of the returned rows.

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?

A single compact sentence delivers the resource, filtering mechanism, ordering, and intended use case with no filler. The dataset scope is front-loaded and every part of the sentence adds value.

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 tool with no output schema, the description is mostly adequate, but it leaves out the output shape and does not explicitly contrast with dataset_search or dataset_row. With no annotations to provide context, these are moderate gaps.

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 convey the key relationship between `column` and `values`: rows are selected when the column equals one of the values, and output order follows the values array. It adds ordering semantics beyond the schema, though it could more explicitly define the parameters.

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 exactly what the tool does: return rows from the FMlane dataset filtered by a column matching any of the supplied values, in the provided order. The 'X vs Y' framing and the sibling-tool names make it clearly distinct from dataset_row, dataset_search, and dataset_top.

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 clear context for when to use this tool ('X vs Y' questions), which helps an agent choose it among the dataset_* siblings. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

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