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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value beyond that by disclosing the output shape (raw value paired with label), the label fallback logic for external_customer_id, and that it mirrors a GET endpoint. This is meaningful behavioral context without contradicting annotations.

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?

Three efficient sentences with the core purpose front-loaded, followed by output details and endpoint reference. Every sentence earns its place; there is no filler or repetition of schema fields.

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 list operation with strong annotations and only three params, the description covers purpose, when to use it, output entry structure, and label semantics. It does not discuss pagination or response limits, but that is not critical for a discovery-oriented list tool and the description is otherwise complete enough to invoke correctly.

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 compensate. It explains the dimension enum values and the role of start_date/end_date as a date range, but it does not specify date formats, optionality, or edge-case behavior. The parameter names are self-explanatory and the description adds enough for basic invocation, but gaps remain.

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 names a specific verb and resource: listing distinct values for a usage dimension over a date range. It also distinguishes the tool from query_usage by stating it is useful for discovering filter values before querying, and the endpoint mirror adds precise grounding. This clearly differentiates it from sibling list tools like list_dimensions.

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 gives explicit context: use this to discover filter values before calling query_usage. It does not enumerate when-not-to-use cases or name alternatives like list_dimensions, but the intended workflow is clear and sufficient for an agent to select it appropriately.

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