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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds useful context by listing the exact supported model families and that the result is a USD cost figure. It doesn't disclose edge cases like currency rounding or pricing data freshness, but the annotation coverage lowers the burden.

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?

Two sentences, front-loaded with the core purpose and followed by a concise support list. No filler or repetition.

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 and simple inputs, the description covers the essential context: what is calculated, the currency, and the list of supported models. It could mention the source/timing of pricing data, but it is sufficient for selecting this tool among many siblings.

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 coverage is 100% and all parameters are described with meaningful examples. The description adds only the general 'token counts' phrasing and the model list, which does not materially improve on 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 uses a specific verb ('Estimate') and resource ('API cost in USD') and states the inputs (model and token counts). It is clear and distinct from siblings like count_tokens, though it does not explicitly differentiate from token_budget_calculator.

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 intended use is implied: call this when you need a cost estimate for a given model and token counts. However, there is no explicit guidance on when not to use it or which sibling tool might be preferable (e.g., model_info, count_tokens).

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