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

With no annotations, the description carries the behavioral burden. It discloses meaningful traits: matching is case-insensitive, all cells are searched, and the result set is capped at 50 rows. It does not describe output structure or ordering, but the core behavior is transparent.

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 16-word sentence with no filler. It front-loads the operation and result, then appends the two most important modifiers: case-insensitivity and the 50-row cap.

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 with no output schema, the description covers the essential facts an agent needs: what is searched, how matching behaves, and the maximum result count. Details like ordering or pagination would be nice but are not required for a correct first call.

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 description coverage is only 50%, but the description compensates by explaining the query as a case-insensitive cell match and conveying the limit's effect as an implicit cap. The limit parameter is not named explicitly, yet its meaning is recoverable from 'up to 50.'

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 and resource: rows of the Runwayze dataset that contain the query. It also adds concrete behavior (case-insensitive, up to 50), making it easy to distinguish from sibling tools like dataset_row or dataset_stats.

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 clearly implies when to use it — when you need to find rows by matching text in any cell — but it gives no explicit exclusions or guidance relative to sibling tools. The usage context is inferable rather than stated.

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