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

With no annotations provided, the description carries the burden and does disclose meaningful behavior: case-insensitive matching, substring containment semantics, and a 50-row cap. It does not mention ordering, pagination, or empty-result behavior, but the key behavioral traits of a search tool are covered.

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 sentence that front-loads the core behavior and includes the limiting constraint with zero filler. Every word 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 search tool with two parameters and no output schema, the description is largely sufficient: it states what rows are returned, the matching rule, and the cap. Minor gaps like sorting and pagination are not critical for a basic search operation.

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 only 50% (query documented, limit not). The description adds value by explaining query semantics ('cells contain the query', case-insensitive) and the effective meaning of the limit bound ('up to 50'). This compensates for the schema gap.

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 uses a specific verb ('search' implied), names the resource ('Rows of the DoorsetBook dataset'), and defines the matching behavior ('cells contain the query, case-insensitive, up to 50'). This clearly differentiates it from siblings like dataset_get_rows (row retrieval) and dataset_columns (schema/column info).

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 makes the intended use clear — full-text substring search across all cells — but does not explicitly state when to prefer this over sibling tools or when not to use it. Usage context is implied rather than stated.

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