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

With no annotations, the description carries the behavioral burden and does it well: it discloses case-insensitive matching, substring containment in any cell, and a 50-row cap. It does not mention ordering or output format, but for a read-only search tool the key behavioral traits are present.

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?

One sentence conveys the resource, the matching predicate, case sensitivity, and the row cap. There is no filler or redundant restatement of the tool name.

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, the core behavior is well covered. However, with no output schema, the description does not clarify whether returned rows contain all columns, the ordering, or the exact effect of the limit parameter, leaving some ambiguity for an agent.

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 already documents query as 'text to look for in any cell', and the description adds the useful case-insensitivity detail. However, the limit parameter is only implicitly referenced via 'up to 50'; the description does not explicitly explain that limit controls the maximum number of returned rows.

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 EmployeeDeskHQ dataset containing the query text in any cell, case-insensitively, up to 50 rows. This clearly distinguishes it from sibling tools like dataset_row, dataset_stats, or dataset_top, which serve different lookup or aggregation 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 use case is clear: call this tool when you need rows matching arbitrary text across the dataset. It does not explicitly name alternatives or exclusions, but no sibling tool performs the same cell-substring search, so the context is sufficient.

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