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

No annotations are provided, so the description carries the behavioral burden. It discloses case-insensitive matching and the 50-row limit, but does not mention return format, default limit behavior, what happens with no matches, or whether all columns are searched.

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, information-dense sentence covers the resource, matching rule, case sensitivity, and result cap. No filler or repetition.

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 two-parameter search tool with no output schema, the description covers the essential semantics: row-level search, query matching, case-insensitivity, and result cap. Minor missing details like default limit or sorting keep it from a 5.

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%; the query parameter is described in the schema, and the description adds case-insensitivity. The limit parameter is not described in the schema, but its maximum of 50 is mirrored in the description. The description adds only modest value beyond the schema.

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 states a specific operation: returning rows of the Csatzo dataset whose cells contain the query, with case-insensitivity and a 50-row cap. This clearly distinguishes it from sibling tools like dataset_row, dataset_stats, or dataset_columns.

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 implies a clear use case: search for rows matching text in any cell. It does not explicitly mention alternatives or when not to use it, but the context is clear enough for an agent to select it for row-level text matching.

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