list_cancel_reasons

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

With no annotations provided, the description carries the behavioral disclosure burden. It exposes a non-obvious constraint: the catch-all reason with needs_comment=true is refused without a comment. It also notes that reason_id is returned, which is essential for a subsequent cancel_order call.

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 only two sentences, with the main purpose front-loaded and a caveat appended. Every word contributes meaning; there is no redundant or filler content.

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 output schema and parameter schema cover return structure and input format. The description adds purpose and a key behavioral caveat. The only minor omission is an explicit connection to the cancel_order workflow, but that is easily inferred from the sibling list.

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 the parameter schema already explains the order number format and that detail_link is accepted. The description adds no parameter-level information, so the baseline score of 3 is appropriate.

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 identifies the tool's resource ('Reasons Ozon will accept for cancelling an order') and the key output ('reason_id'). It distinguishes the listing intent from the sibling cancel_order, which actually performs the cancellation.

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 usage when preparing to cancel an order, but it does not explicitly state 'use before cancel_order' or mention any exclusions. The relationship to cancel_order is inferable from sibling names rather than stated in the description itself.

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