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

No annotations are supplied, so the description must carry the full behavioral disclosure burden. It mentions only ordering; it omits whether the operation is read-only, how matching works, whether values are exact or case-sensitive, and what the response contains.

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 definition is a single compact sentence with no filler and puts the result set first. Its fragmentary phrasing and trailing use-case clause make it slightly less clear than a well-formed front-loaded sentence.

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 filter tool, the core selection logic and intended question type are present. Because there is no output schema, the description could do more to explain what a "side by side" result looks like and how matching is performed.

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?

With 0% schema description coverage, the description does explain the two parameters conceptually: "column" is the field to match and "values" are the values to match in order. It does not clarify exact matching semantics, duplicate handling, or that values is capped at 10.

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 exact dataset and row-selection rule: rows whose column matches any supplied value, preserving the supplied value order for X-vs-Y questions. It is clear enough to understand the operation, though it does not explicitly distinguish itself from siblings like dataset_search or dataset_row.

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 implies use for "X vs Y" questions, which gives a contextual usage hint. However, it offers no explicit when-not-to-use guidance and does not name a sibling alternative for simpler lookups.

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