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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety and idempotency profile is fully covered. The description's only additive behavioral detail is "with source clocks," implying timestamped/provenance-aware data, which is modestly useful but not rich context.

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 front-loaded sentence with no padding or repetition. It is efficient, though the compressed jargon slightly reduces readability.

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 parameters and no output schema, the description carries the burden of conveying what is actually returned, and it only gestures at this ("attributed AI-model usage," "retail e-commerce context," "source clocks") without defining scope, coverage, or time window. Adequate at minimum, but not complete.

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 per the rubric a baseline of 4 applies; there is no parameter semantics for the description to compensate for.

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 verb ("Read") and a resource ("attributed AI-model usage and U.S. retail e-commerce context"), so the general category of operation is clear. However, the phrasing is dense jargon and gives no indication of what distinguishes this from the sibling tools (wake_collection_contract, wake_dataset_policy), which also appear data-oriented.

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?

There is no statement of when to call this tool, when not to, or which sibling to prefer for related data. The agent is left to infer usage from the name alone.

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