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

No annotations are provided, so the description carries the full disclosure burden. It usefully discloses ordering behavior ('in the order given') and selection semantics ('any of the given values'), but it omits details like exact-match vs substring, case sensitivity, or return format. This is adequate but not rich.

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 packs resource, filtering behavior, ordering, and intended use case with no filler. Every clause earns its place.

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 2-parameter tool with no output schema, the description covers purpose, selection logic, ordering, and use case. It does not describe the result format explicitly, but the title 'Compare rows side by side' implies it, making the overall context sufficient for selection and invocation.

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 add parameter meaning. It does: 'column' is defined as the matching field and 'values' as the set of values that select rows, with the order of values driving output order. This adds value 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 states a specific resource (rows of the Hreflangly dataset), an action (select rows whose column matches given values), and a clear use case ('X vs Y' questions). It does not explicitly name sibling tools, but the behavior is distinct enough to be understood.

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 usable when-to-use context, signaling comparison-oriented queries. It does not explicitly contrast with siblings like dataset_search or dataset_row, so it falls short of full guidance.

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