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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds meaningful context beyond those: outcomes are 'caller-reported', provider identities are exposed 'only through public aliases', and outcomes are 'not described as independently verified'. These are important trust and privacy caveats that an agent cannot infer from 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?

Four sentences, each adding distinct value: purpose, usage boundary, identity handling, and content/verification caveats. The core purpose is front-loaded, and there is no fluff or repetition of schema details.

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?

Given the tool's low parameter complexity, existing annotations, and the presence of an output schema, the description is complete. It covers what the tool returns, what it is for, what it excludes, and important data-quality caveats, so an agent has enough to call it correctly.

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?

Schema description coverage is 0%, and the description does not mention the 'limit' parameter. However, the schema's optional integer parameter with default 100 makes the semantics reasonably self-evident: it limits how many recent outcomes are returned. The description adds no narrative around the parameter, but the single parameter is simple enough that this is not a serious deficiency.

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 specific verb ('Read'), a specific resource ('recent caller-reported production outcomes from the AOPX public evidence surface'), and explicitly scopes it away from recommendation ('not to request a new recommendation'). It clearly separates the tool from recommend_provider and report_outcome even though it does not directly contrast with public_stats.

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 use case ('Use this to inspect what was reported after real recommendations were executed') and an explicit negative case ('not to request a new recommendation'), routing agents away from the recommendation sibling. It also states content exclusions (LAB benchmarks, tests, shadow runs, replays), making the tool's boundaries clear.

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