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

Annotations already mark the tool as read-only, idempotent, and non-destructive, and the description adds valuable behavioral details beyond that: it reads stored/own data, costs no AI credits, lists free operations explicitly, shows refunds as negative credits, and reconciles with the plan meter. No contradiction exists.

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 compact and front-loaded with the core purpose, followed by useful specifics: reconciliation, free operations, refunds, and cost. The opening sentence is a bit dense, but every clause contributes meaningful information and there is no 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?

With an output schema present, the description doesn't need to enumerate return fields. It fully covers the data grouping, granularity, reconciliation, free operations, refund behavior, and the cost implication, giving an agent everything needed to understand and invoke the tool 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?

The only parameter, user_intent, is optional and fully described within the schema with a maxLength and explanation. The description itself adds no additional parameter semantics, but schema coverage is 100%, so the baseline of 3 applies.

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 what the tool does with specific verbs and resources: retrieving AI credit usage for the current billing period, including customer-language groups (items) and a per-feature ledger (features). It also clarifies the granularity as per-agent/per-action, which distinguishes it from sibling tools focused on articles, pages, or analytics rather than billing/credits.

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 clear context about when to use this tool: when integration-level granularity of credit usage is neededharee, and it notes that the total reconciles with the plan meter. It doesn't explicitly name an alternative or exclusion, but there is no close sibling tool, so the guidance is sufficient.

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