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

With no annotations, the description carries the full behavioral disclosure burden. It usefully reveals that rows are selected by membership in the given values and returned in the given order, but it does not disclose exact-match semantics, handling of missing values, output format, or error behavior.

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, tightly written sentence that front-loads the core behavior and closes with the intended use case. There is no filler, and every clause 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 essential selection and ordering behavior, but with no output schema and no annotations, the lack of detail about the returned structure and edge cases leaves some ambiguity. It is adequate but not comprehensive.

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 for the schema's bare parameter definitions. It names both parameters and adds meaningful semantics: the column is the filtering field, and the values determine both inclusion and output ordering. Still, it could clarify that 'column' is an existing column name and what value formats are expected.

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 the Opexvo dataset filtered by values in a given column, preserving the provided order. It also signals its distinctive niche ('X vs Y' questions), which partially distinguishes it from siblings like dataset_row or dataset_search, though it does not explicitly name them.

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' gives a clear situational context for when this tool is appropriate. However, it does not explicitly state when not to use it or point to alternatives among the sibling tools, so it stops short of full routing guidance.

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