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

With no annotations, the description carries the burden of behavior. It discloses the core filtering rule and that output follows the order of the given values, which is useful. However, it does not state whether matches are exact, whether full rows are returned, or what happens with no matches.

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, focused sentence fragment that front-loads the core behavior and appends the use case. No filler or repetition.

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 this is mostly sufficient, and the schema supplies parameter shapes. The absence of an output schema and annotations leaves some ambiguity about the return format and matching behavior, so it is not fully 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 explain both parameters. It does clarify that 'column' is the field to match and 'values' are the candidate values whose order controls output. This adds meaningful semantics beyond the bare schema.

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 (Curtilo dataset rows), the selection mechanism (rows whose column matches any of the given values), and the ordering constraint. It is clear enough to distinguish from sibling tools like dataset_search, though it does not name an alternative explicitly.

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?

"For X vs Y questions" explicitly states the intended use case: comparing specific values in a column side by side. It does not list exclusions or alternative tools, but the context is clear.

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