cancel_orderRequest cancellation for an eligible customer order after signed-in browser approval.
| Name | Required | Description | Default |
|---|---|---|---|
| reason | Yes | ||
| order_id | Yes | ||
| confirmation_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive and non-idempotent behavior, and the description adds useful context about the need for signed-in browser approval and the eligibility constraint. It does not contradict the annotations and provides extra behavioral prerequisites beyond the structured metadata.
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 concise, front-loaded sentence conveys the core action, target, and prerequisite without redundant wording. Every word adds relevant 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 presence of an output schema and annotations covers return values and destructive nature, and the description addresses approval and eligibility. However, it leaves undefined what makes an order 'eligible' and how the agent should verify signed-in browser approval, which is important for a destructive mutation tool.
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%, and the description does not explain the roles of order_id, reason, or confirmation_id. The term 'eligible customer order' indirectly relates to order_id, but the description fails to compensate for the lack of parameter-level documentation.
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 states a specific action (Request cancellation), a specific resource (an eligible customer order), and a prerequisite (after signed-in browser approval). This clearly distinguishes it from sibling tools like cancel_payment_attempt by targeting orders rather than payment attempts.
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 context by mentioning 'eligible' and 'after signed-in browser approval', which implies when it should be used. However, it does not explicitly contrast with alternatives or state when not to use the tool, such as when an order is ineligible or approval has not been obtained.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools are clearly separated by resource and action (cart, orders, tickets, picks, payments), so an agent can generally select the right one. The only notable ambiguity is resume_payment vs retry_payment, which both describe acting on an unpaid hosted payment attempt with nearly identical wording.
All 29 tools follow a consistent snake_case verb_noun (or verb_preposition_noun) pattern with standard verbs like get, list, create, close, remove, and set. There are no mixed conventions or vague generic names.
At 29 tools this is a heavy surface, but the broad e-commerce scope (catalog, cart, checkout, payment, orders, support, rewards) justifies most of them. A few payment-attempt tools could be consolidated, so it sits at the overbuilt rather than absurdly bloated end.
The set covers the main customer journey from browsing/searching sarees through cart, checkout, payment status, orders, and post-purchase support. Minor gaps exist—for example, no explicit apply_coupon/redeem_points tool or standalone catalogue listing—but agents can work around them with validate_coupon, checkout summary, and search.