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

With no annotations, the description carries the behavioral burden. It discloses case-insensitive matching, whole-row returns based on cell content, and a result cap of 50. These are useful behavioral details not otherwise available.

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 concise sentence with no filler, front-loading the core behavior before the limit. Every clause contributes necessary information.

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, the description gives enough context: matching rule, case sensitivity, result size cap, and the fact that rows are returned. Minor omissions like ordering or empty-result behavior are acceptable at this complexity level.

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?

Schema description coverage is only 50%, so the description adds real value by clarifying the query matches cells case-insensitively and that results are rows up to 50. The limit's meaning is reinforced beyond the schema's min/max constraints.

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?

States a specific action (search) and resource (Vatnix dataset rows) with precise matching semantics: cells containing the query, case-insensitively, up to 50 results. This clearly distinguishes it from sibling tools like dataset_stats or dataset_columns.

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 makes the usage context clear: use this tool to find rows whose cells match a query. It does not explicitly exclude alternatives or name siblings, but the row-search scope is unambiguous enough for an agent to select it.

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