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

The core behavior is described (filter rows by equality), but it does not disclose return format, error handling, or side effects. With no annotations, the description carries the burden, yet it omits details like what happens if the column does not exist or if multiple rows match.

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 concise sentence that immediately conveys the tool's purpose and key matching semantics without unnecessary detail.

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 read operation, the description is largely sufficient: it specifies the dataset, matching condition, and case-sensitivity. However, it could mention explicitly that multiple rows may be returned or that the result is a collection, though 'rows' implies this.

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 references both parameters ('column' and 'value') contextually, but schema coverage is 0% and no explicit parameter descriptions are given. It implies 'column' is a column name and 'value' is the match target, but does not clarify types, allowed values, or validation rules.

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 retrieves rows from the Ninebix dataset where a specified column exactly matches a given value, with case-insensitivity noted. This distinguishes it from sibling tools like dataset_search and 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 description implies usage for exact-match lookups but does not explicitly mention when to avoid it or alternative tools. The phrase 'exactly' and 'case-insensitive' hint at differentiation from fuzzy search, but no direct guidance is provided.

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