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

With no annotations, the description carries the burden of behavioral disclosure. It states the matching rule (exact, case-insensitive) but omits what happens when multiple rows match, whether one or all are returned, or the behavior for no match. This is a minimal but non-tautological disclosure.

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 leads with the core action and includes the key qualifier (case-insensitive). No filler or redundant phrasing; every word contributes to meaning.

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?

The tool is simple, but the description lacks explicit differentiation from siblings and does not describe the return format or edge-case behavior. Given no annotations and no output schema, a bit more context (e.g., all matches returned, empty result handling) would make it more 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 0%, so the description must explain the parameters. It does by phrasing 'a column equals a value', clarifying that 'column' is the field name and 'value' is the lookup value. However, it doesn't add format details or constraints beyond 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 clearly states the tool returns rows from the Ppmly dataset where a column exactly equals a value, and the case-insensitive qualifier adds specificity. It is distinct from siblings like dataset_search (which implies fuzzy search) and dataset_compare, so an agent can readily tell what it does.

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 exact matching but does not explicitly state when to use this tool over siblings. It never mentions 'use this for exact matches, use dataset_search for fuzzy', leaving the decision to inference from the wording.

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