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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals the matching rule (case-insensitive) and the result cap (up to 50), which are useful. However, it does not mention whether the operation is read-only, what happens with zero matches, or if there is any default ordering. Some transparency is present, but gaps remain.

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?

The description is a single, focused sentence with no redundancy. It front-loads the core action (searching rows) and immediately provides essential constraints (case-insensitivity and limit). Every word adds value.

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 tool with only two parameters and no output schema, the description covers the main behavior but leaves ambiguities: the default limit when the optional 'limit' parameter is omitted, the structure of returned rows, and any error handling. Given the low complexity, this is acceptable 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?

Schema description coverage is 50%: only the query parameter has a description in the schema, while limit does not. The tool description hints at the limit's role with 'up to 50', adding some semantics beyond the schema, but it does not explicitly explain the limit parameter or clarify default behavior. This partial compensation justifies a baseline 3.

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 states the tool's function: returning rows of the Ninebix dataset that contain a query string, with specific details on case-insensitivity and a maximum result count. This distinguishes it from sibling tools like dataset_stats (statistics) or dataset_columns (schema), making the purpose unambiguous.

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 when to use it (searching dataset rows by content) but does not explicitly mention alternatives or exclusion criteria. An agent can infer it is for content-based lookup, but there is no explicit guidance on when to prefer other tools like dataset_top or dataset_row.

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