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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description goes further by naming the underlying dataset size and enumerating the three return values. With no output schema present, that return-value disclosure is genuinely useful; only the handling of unknown museum names is left unstated.

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 the core question the tool answers, then the data source and outputs, then a compact example. Nothing is redundant or padded.

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 small read-only calculator with full schema coverage and no output schema, the description supplies the missing piece: what the result contains. It falls slightly short by not indicating that age and visit frequency change the verdict or what an unrecognized museum name produces.

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 all three parameters are already documented, and the description adds little beyond restating the museum-array shape with a duplicate example. The mention of 32 museums implicitly reinforces the maxItems cap, but the age and visits_per_year parameters go unmentioned in the description.

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 computation (whether a Museumkaart pays off), the domain it applies to (Amsterdam museums), the data behind it (ticket prices for 32 museums) and the outputs it produces (ticket total, card price, saving). No sibling tool does anything comparable, so an agent can route to it unambiguously.

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 context is clear: it is for someone planning museum visits who wants a break-even verdict, and the 32-museum price list implicitly bounds which museums are valid inputs. There is no explicit when-not guidance (e.g. what happens for museums outside the dataset), but with no overlapping siblings there is little alternative to disambiguate against.

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