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

The description discloses two important behaviors: rows are selected when the column value is any of the provided values, and the result order follows the order of the given values. With no annotations provided, the description carries the burden of behavioral disclosure, and it does not explain matching semantics (exact match vs. substring), return format, or potential limitations.

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 entire description is one compact sentence that conveys the dataset, matching rule, ordering, and use case without repeating the title. The structure is slightly awkward because it starts with a noun phrase rather than a verb, but it is efficient and free of filler.

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 core selection logic, ordering, and intended use case. However, with no output schema and no annotations, important details such as the return shape, whether the output is truly 'side by side,' and exact-match behavior are absent. The description is adequate but not fully complete.

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?

The description maps both parameters to their roles: 'column' is the field being matched, and 'values' are the allowed values to match against. It also clarifies that the order of 'values' determines the output order. Since schema description coverage is 0%, this is valuable parameter-level context, though exact-match behavior is not explicitly confirmed.

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 (BurdenRateLedger) and the operation: selecting rows whose column matches any of the given values. It also signals its comparison use case with "for X vs Y questions," which helps distinguish it from siblings like dataset_row or dataset_search. There is no explicit verb like "returns," but the intent is clear.

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 clear context for when this tool is appropriate. However, it does not mention alternatives, such as using dataset_search for broader queries or dataset_row for single-row lookup, nor does it state when not to use it. The usage guidance is implied rather than explicit.

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