stats

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

With no annotations, the description must carry the full behavioral burden, yet it says nothing about whether results are cached, whether the operation is read-only, its cost, or how the category distribution is structured. For a zero-parameter aggregation endpoint these omissions matter.

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 tight sentence that front-loads the verb and resource, with the enumerated metrics acting as scope. No filler, though it is terse to the point of under-specification.

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?

The existence of an output schema relieves the description of explaining return shapes, and there are no parameters to document. However, for a statistics endpoint over a complex knowledge base there is no mention of read-only behavior or when it should be preferred, leaving a gap given the absence of annotations.

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?

Parameter count is zero, so there are no parameter semantics to convey and the baseline is 4; the description correctly does not fabricate parameter discussion.

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?

States a specific action (return statistics) and resource (knowledge base) with enumerated sub-metrics (lecture/knowledge point/marker counts, distribution by category). This is clearer than a bare name but does not explicitly differentiate itself from siblings like list_lectures or list_markers, which also enumerate counts of the same entities.

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?

No guidance on when to use this aggregation tool versus the sibling list_* tools that would return the underlying items. An agent must infer that this is the 'counts-only' option.

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