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

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully discloses case-insensitive matching, cell-level search, and the maximum result count, but it does not mention response shape, pagination, ordering, or match semantics beyond substring containment.

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 compact sentence conveys the resource, matching rule, case sensitivity, and result cap without filler. The most important behavioral constraints are front-loaded and easy to parse.

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 with no output schema and no annotations, the description covers the essential behavior but leaves out return format details and explicit guidance about choosing this tool over sibling dataset tools. It is adequate for basic invocation but not fully complete.

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 the query parameter, and the description adds useful context such as case-insensitivity and the 50-row cap. The limit parameter is only described by its numeric constraints in the schema; the description partially compensates by mentioning the cap but does not explicitly explain how the limit parameter controls the result count.

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 defines the tool as returning rows from the DailyLogDesk dataset where any cell contains the query, with case-insensitive matching and a 50-row cap. This is distinct from sibling tools like dataset_columns, dataset_stats, and dataset_row, so an agent can identify this as the full-text search operation.

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 usage context is implied: use when you need rows matching arbitrary text in any cell. However, there is no explicit statement of when to use this tool over siblings or when not to use it, leaving some selection reasoning to the agent.

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