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

The description discloses useful behavioral details beyond the title, including case-insensitive matching and the 50-row limit. But with no annotations and no output schema, it leaves unspecified what a result row looks like, whether the limit is a default or a hard cap, and how empty results are handled.

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 delivers the matching semantics, case sensitivity, and result cap with no filler. The behavior is front-loaded and every phrase adds information.

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 read-only search over a dataset, the description provides the essential operational facts: what is searched, how matching works, and the maximum result size. It does not document the result fields, but the tool's simplicity and the absence of an output schema keep this from being a major gap.

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 description adds meaning to the query parameter by stating case-insensitive cell containment, which is not fully captured by the schema's one-line description. The limit parameter, however, is only covered by the schema's min/max values; the description does not clarify whether 'up to 50' is the default, a hard cap, or tied to the limit parameter.

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 exactly what the tool returns: rows of the Deflectvo dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly separates it from the dataset_stats, dataset_top, and dataset_columns siblings by establishing it as the content-search operation.

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?

Usage is implied from the description: an agent can infer it should call this when it needs rows matching a string in any cell. However, it does not explicitly state when to prefer this over dataset_row, dataset_columns, or the other sibling tools, and it gives no exclusions.

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