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

With no annotations provided, the description carries the disclosure burden. It transparently states what is counted and references the exact response shape of GET /api/v1/public/stats. This is clearly a read-only query, and no hidden side effects are implied.

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 two short sentences. The first sentence delivers the purpose and object types; the second adds return-shape context. There is no filler, repetition, or irrelevant detail.

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 zero-parameter, low-complexity stats tool, the description fully covers what it returns and the response shape. The sibling tools are straightforward, so an agent has enough information to select and invoke this tool correctly.

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 has zero parameters, so there is no parameter documentation gap. The rubric's baseline for a 0-parameter tool is 4, and the description needs to add nothing further about parameter meaning.

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 starts with a specific verb and resource: 'Get aggregate catalogue counts' and enumerates the counted items (courses, curricula, languages, knowledge components). This clearly distinguishes it from the sibling tools list_courses and get_course_detail, which focus on individual or listed courses.

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 makes clear that this is the aggregate stats tool, not a list or detail tool. It does not explicitly name alternatives or say when not to use it, but the contrast with the sibling names and the aggregate wording gives sufficient contextual guidance.

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