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

With no annotations, the description must carry behavioral disclosure. It usefully reveals that matching is any-of and that output order follows the input values order. However, it does not describe the return format, whether all matches are returned, pagination, or exact-match/case-sensitivity 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, front-loaded sentence that conveys the resource, filter, ordering, and intended use case with no filler. Every clause contributes meaning.

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?

Adequate for a two-parameter read-style tool, but with no output schema and no annotations the description should say more about the returned row shape, the 'side by side' presentation from the title, and edge cases such as no matches or duplicate values. It leaves some operational ambiguity.

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%, but the description directly maps 'column' to the field to filter on and 'values' to the list of values to match, and it adds the crucial semantic that the row order follows the given value order. This goes beyond the bare schema names.

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 (rows of the Monthendly dataset), the filtering action (column matches any given value), and the ordering behavior. The 'X vs Y questions' phrase gives a clear use case, but the description lacks an explicit verb like 'returns' and does not differentiate from sibling tools such as dataset_row or dataset_search.

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?

It explicitly scopes usage to 'X vs Y questions', which tells an agent when this comparison tool is appropriate. It does not state when not to use it or name alternatives, but the use-case cue is clear enough for selection.

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