rank_departments

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

With no annotations, the description must carry the behavioral disclosure burden. It does convey the core output behavior—producing rankings by specified criteria—which is meaningful. But it does not explain how confidence filtering works, how ordering is applied, what the returned ranking structure looks like, or whether the operation is read-only or potentially expensive.

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 a single, front-loaded sentence with no filler. Every word contributes to explaining the tool's purpose and ranking dimensions, making it appropriately concise.

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 no annotations, no output schema, 0% schema description coverage, and an unconstrained metric parameter, the description is not complete enough for confident invocation. It omits acceptable metric values, the effect of the optional parameters, and the shape of the result, all of which matter for correct use.

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 0%, so the description must compensate. It hints at the meaning of 'metric' by listing ranking dimensions, but it does not provide valid metric values, and it gives no semantic explanation for limit, descending, or min_confidence. The agent would have to guess at the expected metric string and the meaning of min_confidence.

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 uses a specific verb ('rank') and resource ('departments') and lists the ranking dimensions: quality, ease, cheating environment, variance or draw risk. It is clear enough to separate this from the sibling 'rank_teachers' by resource, though it does not explicitly contrast with any 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?

The description implies when to use this tool: when you need departments ranked according to one of the listed criteria. However, it gives no explicit guidance about when to prefer rank_departments over related tools like get_department_analytics, search_departments, or compare_departments, and it does not state any exclusions.

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