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

With no annotations, the description carries the behavioral disclosure burden and handles it well by revealing case-insensitive matching, cell-wide search scope, and a 50-row upper bound. It does not mention default limit behavior or result ordering, but these are minor for a simple read-only search 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 communicates the essential behavior, scope, and limits without wasted words. Every clause contributes 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 search tool with a small schema and no output schema, the description gives enough context for an agent to invoke it correctly. It could mention the default limit value or explicitly note the read-only nature, but the core semantics are fully covered.

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 only documents the query parameter in detail; the description compensates by explaining that query applies to any cell and by tying the limit to 'up to 50'. This adds meaningful semantics beyond the bare parameter names and ranges.

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 states a specific verb-resource relationship: it returns rows of the LPreportly dataset matching a query across any cell. The case-insensitive and 50-row cap details further distinguish it 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 intended use is implied by the description: search for rows containing text in any cell. However, it does not explicitly state when to prefer this tool over sibling tools such as dataset_row or dataset_top, nor does it list any exclusion criteria.

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