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

With no annotations, the description carries the full burden. It usefully discloses case-insensitive matching and the 50-row limit, but it does not explain how rows are ordered or selected when more than 50 match, nor what the returned row objects contain.

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, front-loaded sentence states the core behavior and key constraints with no wasted words. It is concise without sacrificing the most important operational details.

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 tool, the description is mostly adequate: it explains the query semantics and the result cap. However, with no output schema and no usage guidance, it leaves uncertainty about result ordering, which 50 rows are selected, and how to choose this tool over siblings.

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 coverage is only 50%: the query parameter has a description, while limit does not. The description adds meaningful semantics by stating case-insensitive cell matching and the 50-row cap, though the limit information largely duplicates the schema's maximum value.

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 the operation: find rows of the Patientvo dataset whose cells match a query, with case-insensitive matching and a 50-row cap. It implies row-level search rather than schema/statistics operations, which helps distinguish it from siblings like dataset_columns and dataset_stats, though it does not explicitly name an alternative.

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 use this tool versus siblings such as dataset_row, dataset_top, or dataset_columns. The description implies general text-search usage, but it does not state exclusions, alternatives, or conditions that would select this tool over the others.

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