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

With no annotations supplied, the description carries the whole disclosure burden. It does add real behavioural substance — the two segmentation levels (device vs. model), the source breakdown, the fixed 10% threshold comparison, and the fact that no parameters or cost are involved — but it never states that the call is side-effect free or describes the shape of the returned figures.

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 front-loaded with the resource and then lists the four computed metrics compactly, with the usage trigger and cost note at the end. It is dense but every clause adds information; the only mild cost is the list-like density of the first sentence.

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 tool with no output schema and no annotations, the description does the heavy lifting by enumerating what comes back: real/synthetic counts at two granularities, source list, and threshold shortfall. It is nearly complete for the call decision; only the concrete return format and whether figures are cached or live are absent.

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 input schema is an empty object, so there are no parameters whose meaning could be lost; the zero-parameter baseline applies. The description implicitly confirms this by describing a self-contained, parameterless statistic call.

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 names a specific resource (data provenance / synthetic-data ratio) and enumerates exactly what it reports: real vs. synthetic counts, computed separately at device level and model level, the contributing sources, and the gap to the '≤10% synthetic' threshold. This is far more specific than the name alone, though it does not name or contrast any sibling tool, so an agent must infer the boundary against tools like cvin_verify or cvin_risk_signals.

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?

It gives a clear when-to-use trigger — check this before citing registry-table statistics — plus a cost signal ('免费') that helps prioritise the call. It stops short of exclusions or naming an alternative tool for cases where provenance is not needed, so it is context-rich but not fully routing.

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