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

No annotations are present, so the description carries the behavioral burden. It discloses substring-like matching, case-insensitivity, and a 50-row cap, which is useful. However, it does not clarify whether 50 is a default or absolute maximum, whether results are ordered, or what fields the returned rows contain.

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, front-loaded with the core behavior, and compactly incorporates the key constraints. There is no filler or redundant restating of schema fields.

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 what is searched, what is returned, and the result cap. It lacks an example or explicit ordering/pagination details, but the low complexity and sibling context make this a minor omission.

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 ('text to look for in any cell'), but limit is only described by min/max. The description adds meaning by tying query to cell containment with case-insensitivity and clarifying the 'up to 50' behavior. It does not state the default when limit is omitted, leaving a small gap.

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 identifies the resource (Depreo dataset), the output (rows), and the matching rule (cells contain the query, case-insensitive). It is easy to distinguish from siblings like dataset_columns or dataset_stats, though it does not explicitly name an alternative, so it stops short of maximum differentiation.

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 gives clear context for when to use this tool: when an agent needs dataset rows matching text in any cell. It does not explicitly state when not to use it or point to alternatives, which prevents a 5.

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