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

With no annotations, the description must carry the behavioral burden. It indicates a read-only query by stating it provides statistics, but it does not explicitly declare read-only status or absence of side effects. For a non-mutating statistics tool, this is a mild gap, not a contradiction.

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, dense sentence front-loads the purpose ('Coverage statistics') and enumerates exactly what is covered. No filler words; every phrase contributes meaning.

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 statistics tool, the description lists all the metrics an agent would need. It doesn't specify the return format (e.g., JSON structure), but the enumerated list is sufficient for a caller to know what to expect. The lack of an output schema makes this adequate, not exhaustive.

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 the schema is trivially complete. Per the baseline rule for 0-parameter tools, a score of 4 is appropriate; the description adds no parameter detail because none exists.

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 clearly states it provides coverage statistics and enumerates specific counts (total, per-axis, per-confidence, per-self-report, pre-canonical, unreviewed) and version info. It is specific and actionable, though it doesn't explicitly differentiate from sibling tools like list_axes or get_probe, relying on the statistical nature to stand apart.

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 usage for viewing coverage statistics but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. Context suggests it's the go-to for overall stats, but this is not stated, leaving the agent to infer.

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