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

With no annotations provided, the description carries the behavioral disclosure burden. It usefully discloses case-insensitive matching, cell-level containment, and a 50-row cap. It does not explicitly state read-only behavior or result formatting, but as a simple search tool these are low-risk omissions.

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 contains the resource, matching rule, case sensitivity, and row cap with no filler or redundant phrasing. 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 two-parameter search tool, the description covers the core semantics needed to call it correctly: search by cell content, case-insensitive, maximum 50 rows. It lacks explicit clarification of the default limit and return shape, but given no output schema, the description is still reasonably complete.

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 documents query as 'text to look for in any cell' but leaves limit without a description. The description adds meaning beyond the schema by specifying case-insensitive matching and the 50-row ceiling, which clarifies the overall search behavior even if the default limit is not stated.

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 names a specific resource (HeadcountDesk dataset) and a precise behavior: returning rows whose cells contain the query, case-insensitively, up to 50. This clearly distinguishes it from siblings like dataset_row, dataset_stats, and dataset_top.

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 the tool: when you want dataset rows by arbitrary cell-text matching rather than by row identity or aggregated statistics. It does not explicitly name alternatives or exclusions, but the intended use case is clear.

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