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 reveals case-insensitive substring matching and the 50-row result cap, but does not mention return format, ordering, empty-result behavior, or whether the limit has a default.

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 front-loads the object, action, matching behavior, and cap. There is no filler or redundant repetition of the tool name.

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 no output schema and no annotations, the description conveys the core search behavior but omits result shape, ordering, default limit behavior, and usage context relative to sibling tools. It is minimally adequate but not 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?

Schema coverage is only 50%, with query documented but limit not. The description adds meaning by clarifying case-insensitive matching across any cell and the 50-row cap, but it does not explicitly explain the limit parameter beyond what the schema's min/max already imply.

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 verb and resource: it returns rows of the Huddlevo dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly differentiates it from sibling tools like dataset_columns or dataset_stats.

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: use this to find rows matching a text query. However, there is no explicit guidance on when to prefer it over alternatives such as dataset_row or dataset_compare.

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