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

With no annotations available, the description carries the full behavioral disclosure burden and does reasonably well: it states the selection criterion (column value equals any of the given values) and the ordering behavior (rows follow the order of the given values). It does not explicitly declare read-only status or describe edge cases like no matches, but the core retrieval behavior is clearly conveyed.

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 with no filler, front-loading the substantive behavior and ending with a brief purpose clause. Every clause contributes useful information, making it both concise and well-structured.

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 query tool with no output schema, the description covers the essential invocation details: dataset, column to filter on, values to match, and ordering. It leaves some details unspecified, such as the exact output row shape and behavior for missing or unknown values, but it is adequate for an agent to call the tool correctly in the intended comparison scenario.

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 maps 'column' to the field on which rows are filtered and 'values' to the candidate values whose order determines the result ordering. This adds real meaning beyond the bare JSON schema types and constraints.

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 identifies the operation: returning rows from a dataset filtered by equality against a list of values, in a specific order, to support side-by-side comparison. The title adds the verb 'Compare' and the 'X vs Y' phrasing reinforces the comparison purpose. It does not explicitly name sibling tools, but the semantics are specific enough to distinguish it from a generic search or single-row fetch.

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 provides a clear use context: 'for "X vs Y" questions', indicating when an agent should select this tool for comparison scenarios. It does not enumerate exclusions or alternatives, but the stated purpose is sufficient for basic routing among the sibling tools.

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