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

No annotations are provided, so the description carries the full behavioral burden. It discloses one useful trait—output order follows the given values—but omits read-only nature, error behavior for unmatched values, pagination, and any permission or rate-limit context.

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 single sentence is front-loaded with the resource and scope, and the em-dash use-case is compact. It is slightly awkward as a verbless fragment, but it wastes no words.

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 with no output schema, the description covers the basic retrieval operation. It omits column format, output shape, read-only assurance, and value-count limits, leaving some gaps an agent would need filled.

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 compensate. It partially does by explaining that values are matched against a column and that result order follows the values, but it leaves column format (name vs. ID), matching semantics (case sensitivity, exactness), and the max-10-values constraint unaddressed.

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 names the resource (rows of the Bags That Pay dataset) and the filter condition (column matches any of the given values) plus output ordering. It is clear but does not explicitly distinguish this tool from siblings like dataset_search or dataset_row, so it falls short of a 5.

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 gives an implied use case with 'for "X vs Y" questions' but never states when to use this over dataset_search or dataset_row, nor any exclusions. The guidance is minimal and inferred rather than explicit.

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