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

With no annotations, the description carries the burden of behavioral disclosure. It usefully reveals case-insensitive matching and that any cell is searched, but it does not explicitly state that the operation is read-only, describe the return format, or mention ordering and empty-result behavior.

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 sentence with no filler. The core matching semantics are front-loaded and every clause adds information about the search scope or result cap.

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 no output schema or annotations, the description covers the query behavior and limit cap adequately. However, it omits the default limit value, exact return shape, and any guidance on sibling-tool tradeoffs, leaving minor but real gaps.

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, and the description adds the case-insensitive matching behavior for it. Limit has no schema description, and 'up to 50' only restates the schema maximum, so the parameter's default and effect if omitted are not clarified.

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 a search behavior: rows of the Carbikly dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This is specific in verb and resource, but it does not explicitly distinguish itself from sibling tools such as 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?

No guidance is given about when to choose dataset_search over its siblings, such as dataset_row for a specific row or dataset_stats for summaries. The use case is implied by the name but not stated as a decision rule.

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