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

With no annotations, the description carries the disclosure burden. It reveals important behavior: case-insensitive matching, substring-like cell containment, and a 50-row cap. It does not describe return structure, ordering, or default limit behavior, but the core search behavior is transparent enough for a simple tool.

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, front-loaded sentence packs the essential behavior, matching criterion, case sensitivity, and row cap without extraneous words. Every element 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 two-parameter search tool with no output schema, the description covers the critical behavior: what is searched (cells), how (case-insensitive containment), and the upper bound (50). Minor gaps like default limit and return column set do not seriously impede correct invocation.

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 50%: the query parameter is already described as 'text to look for in any cell', and limit has min/max constraints. The description adds the case-insensitive detail and reinforces the 50-row cap, but it does not clarify the default limit or output shape.

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 is specific and action-oriented: it returns rows of the Opexvo dataset whose cells contain the query, case-insensitively, up to 50. This clearly distinguishes it from sibling tools like dataset_columns, dataset_row, or dataset_stats, which have different purposes.

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 behavior implies when to use it: when you need to find dataset rows by searching cell text rather than by known row identity or column analysis. However, it does not explicitly state when not to use it or mention any sibling alternatives, leaving usage guidance implicit.

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