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

With no annotations, the description carries the behavioral burden. It does disclose case-insensitivity and a 50-row cap, which is useful, but it does not mention return shape, ordering, pagination, or behavior with no matches.

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 behavior, matching semantics, and result cap are all front-loaded.

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 two-parameter search tool, the description is reasonably complete, but the absence of an output schema and any usage alternatives leaves some ambiguity about result format and tool selection. Adequate for basic invocation, not fully self-sufficient.

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 describes the query parameter and constrains limit, but the description adds meaning by noting case-insensitive matching, cell-level search, and the effective 50-row ceiling. This compensates for the lack of a limit description in the schema.

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 operation: returning rows of the Tachovo dataset whose cells contain the query. It is not a tautology and clearly differentiates itself from row-fetching or stats tools by describing cell-level searching.

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 intended use is implied by the description, but there is no explicit guidance about when to choose this tool over siblings like dataset_row, dataset_stats, or dataset_top. An agent must infer it from the search semantics.

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