available_appointments

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

With no annotations, the description must carry the safety profile itself; it discloses that the data is synthetic and adds a guardrail against booking off model output, which is real behavioral value. It stops short of stating that the operation is read-only, whether results are paginated or cached, or how fresh the availability data is.

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 compact sentence with the resource first and the safety directive second — nothing is padded. It is arguably under-sized for a tool that gates a downstream booking sibling, but no sentence is wasted.

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?

An output schema exists and the tool has no parameters, so the description need not cover return values or inputs. Given that low complexity and the presence of the key booking safeguard, what remains is only minor: no explicit direction on how this pairs with propose_appointment.

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 tool takes zero parameters and the schema is empty and fully described, so there is nothing for the description to clarify. Baseline 4 applies; no parameter gaps exist.

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 pairs a concrete verb ("List") with a concrete resource ("clinic appointments") and qualifies the dataset as "synthetic," so an agent knows this is a read-only enumeration over mock data. It also implicitly separates itself from the sibling propose_appointment by deprecating booking behavior, though it never names the sibling.

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?

"Never book from model output alone" gives one explicit usage constraint and hints that booking happens elsewhere, which is meaningful. However, it does not say when to call this versus propose_appointment, whether to call it before proposing, or what preconditions exist, leaving the routing mostly inferred.

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