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

With no annotations provided, the description carries the behavioral burden. It usefully discloses case-insensitive matching and the maximum result cap, but omits behavior like the default limit when not supplied, result ordering, and what fields are included in returned rows.

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 with all essential information front-loaded and no filler. Every clause adds meaning.

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 search, the core behavior is present, but with no output schema the description should clarify more about the returned rows, ordering, and the effect of omitting limit. These gaps could lead an agent to assume an unfounded default behavior.

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 50%: only query has a description. The description reinforces query semantics with 'case-insensitive' and paraphrases limit's maximum as 'up to 50', but it does not add much beyond the schema and does not state the default limit.

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 of a specific dataset where cells match the query, with case-insensitive matching and a 50-row cap. It is a concrete verb+resource description, though it does not explicitly contrast with sibling tools like dataset_top 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?

The intended usage is implied: use this tool when you need dataset rows containing arbitrary text in a cell. However, there is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives such as dataset_stats or dataset_columns.

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