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

With no annotations, the description carries the full burden. It discloses the ordering behavior (rows returned in the order of the given values) and the matching logic (any of the values). However, it does not describe the output format, what happens when a value has no matches, or whether multiple rows per value are returned. This is a moderate disclosure.

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 description is a single sentence that front-loads the core behavior (what rows are returned and in what order) and appends a concise usage hint. There is no wasted verbiage.

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 tool, the description covers the essential behavior: which dataset, which column, which values, and the ordering. It does not detail the return format or edge cases, but given the sibling tool suite and lack of an output schema, this is adequate for an agent to invoke it correctly.

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%, so the description must explain the parameters. It clarifies that 'column' is the field to match against, 'values' are the specific values to filter by, and the order of values dictates the output order. This adds semantic meaning beyond the raw schema types.

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 states the tool returns rows from the Fax Line Index dataset filtered by a column matching any of the given values, in the order given. It also adds the use-case hint 'for X vs Y questions', which distinguishes it from siblings like dataset_row (single row) or dataset_search (broad 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?

The description provides a clear usage context: comparing specific values (X vs Y). It does not explicitly name alternatives or state when not to use it, but the use-case hint and the explicit filtering logic imply when this tool is appropriate compared to siblings.

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