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

With no annotations, the description carries the full burden of explaining behavior. It reveals the filtering rule and ordering behavior, which is useful, but it does not describe the output format, how 'side by side' is represented, or edge cases like missing or duplicate values.

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 sentence with no filler; the core behavior and intended use are front-loaded. Every phrase contributes to understanding.

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?

The tool is simple and the description covers the selection logic, but with no output schema and no annotations, an agent may still be unsure about the exact return shape and how the side-by-side comparison is presented. More detail about output or sibling relationships would strengthen it.

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 coverage is 0%, and the description compensates by defining the roles of both parameters: 'column' is the field to match against, and 'values' are the allowed values whose order affects output order. 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 states a concrete behavior: return rows from the Kbasevo dataset where a column matches any of the provided values, in the given order. This clearly distinguishes it from plain row retrieval, though it does not explicitly contrast sibling tools.

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' provides a clear use context for comparison-style queries. It does not explicitly say when not to use it or name alternatives, but the intended scenario is evident.

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