check_payment_status

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the return values ('success', 'pending', or 'failed'), which is helpful, but it doesn't cover other important aspects such as error handling, authentication requirements, rate limits, or whether it's a read-only operation (implied by 'check' but not explicit). For a tool with no annotation coverage, this leaves significant gaps.

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 extremely concise and front-loaded, consisting of two sentences that directly state the tool's purpose and return values. Every sentence earns its place by providing essential information without any waste, making it easy for an AI agent to parse quickly.

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?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return values, but it lacks details on usage context, error handling, and behavioral traits. Without annotations or an output schema, the description should do more to compensate, but it only meets the minimum viable threshold.

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 schema description coverage is 100%, with the parameter 'orderId' fully documented in the input schema. The description doesn't add any additional meaning beyond what the schema provides (e.g., it doesn't explain format or constraints for 'orderId'). According to the rules, with high schema coverage, the baseline score is 3, which is appropriate here.

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 the tool's purpose: 'Check the payment status of an order.' It uses a specific verb ('check') and resource ('payment status of an order'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'create_order' or 'generate_payment_link', which is why it doesn't reach a score of 5.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an order ID from 'create_order'), exclusions, or how it relates to sibling tools like 'generate_payment_link' for payment initiation. This lack of contextual usage information limits its effectiveness for an AI agent.

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