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

With no annotations, the description carries the transparency burden. It discloses that rows are returned in the order of the given values and that matching is by 'any of' the values. It omits details like response format, exact-match semantics, and behavior when no rows match, so it is only partially 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 sentence and packs the key semantics: target, filter, order, and purpose. It could be improved by adding a verb, but there is no wasted content.

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 tool without an output schema, the description covers the main query behavior. However, it is a noun phrase rather than a full statement of what the tool returns, and it does not address edge cases or result structure, leaving some ambiguity for an agent.

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 is the only source of parameter meaning. It effectively explains 'column' as the column to match and 'values' as the values to filter on, and adds the ordering semantics. It does not elaborate on whether matching is exact or case-insensitive, but the core meaning is clear.

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 identifies the resource (Carbikly dataset rows), the filtering condition (column matching any of the given values), and the ordering behavior. However, it lacks an explicit verb like 'returns' or 'fetches', and it does not explicitly distinguish itself from sibling tools beyond the 'X vs Y' hint.

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 says the tool is 'for X vs Y questions', which provides a clear use case. It does not name alternatives or state when not to use it, so routing among the sibling dataset tools 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.