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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions that rows are returned 'in the order given', which is a useful ordering detail, but it omits other critical behaviors such as whether the operation is read-only, what the response format looks like, or how edge cases (e.g., no matching rows) are handled. This is insufficient for a tool with no annotation support.

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, efficient sentence that conveys the core behavior and use case without any fluff. The key constraint (order given) is stated early, making it front-loaded and easy to parse.

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 the tool has no output schema and no annotations, the description should clarify what the agent can expect. It does not mention the response format, whether all columns are returned, or what happens with empty results. It also lacks any performance or error-handling notes. For a simple two-parameter tool, this is still a notable gap in completeness.

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 implicitly explains both parameters: 'column' is the column to filter on and 'values' are the values to match, and it specifies that the order of 'values' determines the output order. However, schema coverage is 0%, so the description must compensate fully; it provides the core semantics but lacks details like exact matching rules, case sensitivity, or whether the column must be a string.

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 returns rows from the Longtailo dataset filtered by a column matching any of the given values, preserving the order of values. It also specifies the intended use case ('X vs Y' questions), making it easy to distinguish from sibling tools like dataset_search or dataset_row.

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 phrase 'for "X vs Y" questions' gives a clear context for when to use this tool, implying it is for comparing specific values rather than general searching or single-row retrieval. However, it does not explicitly name alternatives or state when not to use it, so it misses full exclusion guidance.

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