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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior. The description adds meaningful scope by enumerating the signals returned and contrasting them with provider claims, which is useful context beyond the annotations.

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?

Three tight sentences: the first defines the output, the second differentiates scope, and the third states usage. Nothing is redundant and the core definition is front-loaded.

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, read-only diagnostic tool with rich annotations, the description covers what the tool reports and when to use it. It could specify the output shape more explicitly, but no output schema exists and the signal list gives sufficient context.

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 takes zero parameters, so there are no parameter semantics to document. Per the rubric, a zero-parameter tool has a baseline of 4.

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 specifies the tool surfaces data-quality signals in the index, naming concrete examples (stub names, missing prices or lesson counts) and explicitly scoping it as scrape health rather than provider claims. It stops short of naming any sibling tool, so sibling differentiation relies on inference.

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 direct usage condition: 'Use it only for maintenance or data questions,' and excludes provider claims. However, it does not identify which sibling tool to use for other data queries, so an agent still has to infer alternatives.

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