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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), but the description adds real context: the defaults come from Recoup's public 60-ticket sample, not customer data, and the result must be framed as an estimate. That provenance and accuracy caveat go beyond what annotations or schema convey.

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 what the tool computes, followed by the data-provenance caveat and the presentation instruction. Dense but each clause carries information; no filler.

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 four-parameter, read-only estimator the description covers source, defaults, and caveat adequately; the absence of an output schema is the main gap, since the description never hints at the shape of the result (single figure vs. breakdown) it tells the agent to present as an estimate.

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 100%, so each parameter's meaning and default is already documented, making 3 the baseline. The description's sample numbers (39 money promises, 21 mismatches, $744.25) corroborate the defaults but add no syntax or semantics beyond 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 gives a specific verb and resource (estimate refund exposure) and narrows the scope to 'monthly money at stake from AI-agent refund mismatches'. That is precise enough for an agent to distinguish it from the sibling retrieval tools (overview, pricing, sources), which return reference data rather than a computed estimate.

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?

Usage is only implied: an agent infers it should call this when it wants a refund-exposure figure, and that supplying 'the brand's own numbers' overrides the sample defaults. There is no explicit when-to-use/when-not statement and no named alternative among the siblings.

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