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

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses case-insensitive matching and a 50-row cap, but it does not mention default limit behavior, ordering, empty-result handling, or whether full row objects are returned.

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, efficient sentence front-loads the key behavior and includes only valuable modifiers. It avoids restating the title or schema redundantly.

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 tool, the description provides enough to invoke it correctly: dataset, query behavior, and result cap. However, with no output schema or annotations, it would benefit from stating the default limit and whether results are full row objects.

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 schema documents query's meaning but not limit. The description adds useful semantics by specifying cell containment, case-insensitivity, and the up-to-50 result cap. The default limit is left unspecified, but both parameters are otherwise understandable.

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 identifies the tool as returning rows from the WallChartBook dataset whose cells contain the query, with case-insensitive matching. This makes it easy to distinguish from sibling tools like dataset_columns, dataset_stats, 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 matching semantics imply this tool is for full-text search across rows, but the description does not explicitly state when to use it over alternatives or mention any exclusions. Usage guidance is present only by inference.

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