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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals two important behaviors beyond the schema: rows are selected by matching any of the given values (OR semantics), and rows are returned in the order given in the values array. It omits output format and limits, but the core behavior is well exposed.

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 operation and selection semantics, then adds the use-case clause. Every word contributes; there is no redundancy or filler.

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 read tool with no output schema and no annotations, the description covers purpose, selection semantics, ordering, and typical usage. It does not describe the exact output format or edge cases, but the simplicity of the tool makes this acceptable.

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%, so the description must explain the parameters. It does: 'column' is the field to match, and 'values' are the set of allowed matching values whose order determines row order. This goes beyond the bare schema by relating the two parameters to the behavior.

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 operation and resource: it returns rows of the Termslane dataset filtered by column membership, with order preserved. The phrase 'for "X vs Y" questions' clarifies its niche and distinguishes it from abstract sibling tools like dataset_search 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 description explicitly scopes usage to 'X vs Y' comparison questions, which tells an agent when to prefer this tool. It does not name sibling alternatives or explicitly state when not to use it, but the context is clear enough for selection.

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