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

No annotations are present, so the description carries the full behavioral burden. It discloses case-insensitivity and the 50-row limit, which are useful. However, it does not describe the return format, ordering, empty-result behavior, or any permission requirements, so some uncertainty remains.

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 that front-loads the main behavior and includes the two most important modifiers (case-insensitive, up to 50). Every word earns its place, with no filler.

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?

Given there is no output schema and no annotations, the description should cover response format and edge cases; it does not. The presence of similar sibling tools also argues for a sentence on when to use this search versus other dataset access tools. Still, the core matching behavior is well specified.

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 description maps the query parameter to cell-content matching and the limit parameter to a 50-row cap, complementing the partial schema. It adds the case-insensitive detail not present in the schema. With only 50% schema coverage, this partially compensates but leaves unspecified what happens when no limit is given.

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 exactly what the tool does: returns rows of the MultiplesBook dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes it from sibling tools like dataset_row (single row) or dataset_stats (aggregates).

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 implies a search use case but does not explicitly state when to prefer this over dataset_row or dataset_columns. There is no mention of alternatives or exclusions, so the agent must infer when this is the right tool. This is adequate but leaves routing to inference.

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