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

No annotations are provided, so the description carries the entire behavioral burden. It does disclose one genuine trait — results come back 'in the order given,' preserving the order of the input values — but says nothing about what happens when a value has no matching row, result limits, or permissions.

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 front-loaded sentence that leads with the resource and ends with the use case. No filler, though the trailing 'for "X vs Y" questions' is somewhat clipped and reads like a fragment.

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 lookup with no output schema and no annotations, the description covers the core selection logic but omits the return shape, ordering caveats beyond input order, and failure behavior. Adequate to call, but not complete.

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 meaning onto both parameters by describing 'the rows whose column is any of the given values,' clarifying that 'values' is matched against 'column' with OR semantics. It adds no format, casing, or value-count guidance beyond what the schema constrains.

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 a specific resource (rows of the Calcul Brut en Net dataset) and the selection rule (column is any of the given values), plus a concrete use case ('X vs Y' questions). It's clear what it retrieves, though it never explicitly distinguishes itself from sibling dataset_search or dataset_row.

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' implies the context of use, giving some guidance. However, it offers no when-not conditions and does not name dataset_search as the alternative for broader filtering, leaving the agent to infer the boundary.

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