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

Since no annotations are provided, the description carries the behavioral disclosure burden. It does reveal case-insensitive matching and a 50-row cap, but it omits the default limit, ordering/pagination behavior, return format, and whether matching is substring or token-based.

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?

The description is a single, front-loaded sentence with no filler or redundancy. Every word contributes to understanding the tool's core behavior.

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, the description is adequate, but without an output schema it leaves gaps around the return format, default limit, and empty-result behavior. An agent could invoke it, but may be uncertain what response shape to expect.

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 covers the query and the limit's min/max range. The description adds 'case-insensitive' for the query and 'up to 50' for result count, but it does not clearly explain the limit parameter's role or default value. With 50% schema coverage, this is partial compensation.

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 a specific verb and resource: it returns rows of the Inductvo dataset whose cells contain the query. It adds key behavioral details (case-insensitive, up to 50) and clearly differentiates this search tool from sibling tools like dataset_row or 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 gives no guidance on when to choose dataset_search over the sibling tools. It does not mention alternatives, exclusions, or the type of query scenario that would call for this tool, leaving the agent to infer usage.

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