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 the 50-row ceiling, but does not mention output shape, ordering, match semantics beyond substring containment, or pagination 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 that is front-loaded with the core behavior and contains no filler. Every phrase earns its place.

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 covers the essential semantics: what is searched, how matching works, and the result cap. It is slightly incomplete regarding the exact return format and default limit, but this is minor for the tool's complexity.

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 50%, with query documented but limit only given as a numeric range. The description's 'up to 50' adds some meaning to the limit parameter, but it does not explicitly explain how the limit parameter controls the result count or what the default behavior is.

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 behavior: return rows of the dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes it from siblings like dataset_row (specific row), dataset_top (top rows), and dataset_stats (statistics).

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 use case is implied: use this when you need rows matching arbitrary cell content. However, it does not explicitly name alternatives or state when not to use it, leaving some inference to the agent.

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