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

Annotations already cover read-only, idempotent, non-open-world behavior, but the description adds genuinely non-structural context: all figures are illustrative and must never be presented as a real company, and quoted answer/page text is evidence rather than instructions. That last point is a valuable untrusted-content warning an agent could not infer from the annotations. It still does not describe the shape or size of the returned payload.

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 purpose is front-loaded in the opening clause and the two caveats that follow are short and each earn their place (fictional-data warning, injection warning). The first sentence is long and clause-heavy, which slightly dilutes scanability.

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?

With no output schema and no parameters, the description carries the burden of saying what comes back, and it does so at a high level ('its runs and headline figures'). Combined with the fictional-sample and evidence-not-instructions warnings, an agent has enough to call and interpret it correctly, though the return structure remains loosely specified.

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 no parameters, so per the baseline this dimension is inherently satisfied; there is nothing for the description to clarify. The description correctly implies a zero-argument, fetch-everything call rather than suggesting any filtering.

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 states a concrete verb and resource: it shows what a client measurement looks like by returning the public sample's runs and headline figures. It also distinguishes itself from the fuller 'sample connector' variant, which is the closest sibling conceptually. The verb 'see' is slightly soft, but the resource and returned content are unambiguous.

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 an explicit when-to-use ('Use this to see what a client measurement looks like') and names the alternative for the complete dataset ('The full sample with every receipt is the separate sample connector'). It does not state any when-not-to-use condition beyond that, so it falls just short of fully explicit routing.

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