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

With no annotations, the description carries the transparency burden. It discloses the key matching semantics ('any of the given values') and the ordering behavior ('in the order given'), which is useful. However, it does not describe the output format, exact-match constraints, or failure behavior, leaving some behavioral details implicit.

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 conveys the core behavior first, then adds the use-case context. There is no filler, and the most important information is front-loaded.

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 two-parameter tool with no output schema, the description explains selection logic and intended use well enough to invoke it. Still, an agent cannot predict the exact response shape or whether rows are actually returned side by side, so some operational detail is missing.

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 0%, but the description adds real meaning for both parameters: 'column' is the field to filter on, and 'values' are the allowed matches whose order drives output ordering. This goes beyond the bare type constraints in the schema.

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 precisely what the tool does: it returns rows of the Lugtorq dataset where a given column matches any of the supplied values, preserving the order of those values. This is specific enough to distinguish it from generic search or single-row tools, though it does not explicitly name sibling alternatives.

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 clause 'for "X vs Y" questions' gives a clear use case for when to select this tool. It implies comparison-oriented lookups, but it does not state when to prefer a sibling like dataset_search or dataset_row, so exclusions and alternatives are not explicit.

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