verify_receipt

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

No annotations are provided, so the description carries the full burden. It discloses the tool's verifcation behavior as read-only confirmation of a mutation, but it does not state whether verification is asynchronous, how receipt IDs expire, what happens if the mutation hasn't propagated, or what the return value looks like. Basic behavioral context is present, but depth is limited.

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?

One sentence, fully front-loaded with the main purpose, and no filler. It earns its place by expressing both the operation and its trigger condition efficiently.

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?

For a one-parameter tool with full schema coverage and no output schema, the description is mostly sufficient. However, because no annotations exist, the agent may benefit from knowing whether verification is best-effort, whether a not-found receipt implies mutation failure or eventual consistency, and what response shape to expect. These are meaningful gaps for an AI agent deciding whether to act on the result.

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 schema describes receipt_id as 'Receipt ID from a receipt-bearing mutation result.' The description adds a crucial semantic layer by clarifying that this parameter is a confirmation artifact for a prior mutation, which helps the agent understand provenance. This is modest value beyond the schema, so a 4 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 states a clear verb-resource pair: 'verify' that a previous mutation 'landed on Spotify' by 'looking up its receipt.' This distinguishes it from the near-sibling receipt_lookup and other mutation-related tools, since it explicitly frames the purpose as post-mutation confirmation rather than generic receipt retrieval.

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 clearly implies when to use this tool: after a mutation that returns a receipt, to confirm the change took effect. While it does not explicitly name alternatives or exclusions, the phrasing 'actually landed on Spotify' provides clear context distinguishing it from pure lookup tools like receipt_lookup.

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