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

Annotations already declare readOnlyHint=true, so the read-only nature is known. The description adds valuable behavioral context beyond the annotation: it specifies that statistics are from the production pilot and that LAB benchmarks, tests, shadow runs, and replay events are excluded. This clarifies data scoping and potential availability limitations, which is useful for the agent. No contradiction with 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?

The description is extremely efficient: two sentences with no fluff. The core purpose ('Read aggregate public statistics...') is front-loaded, and the exclusions and usage guidance are provided in the second sentence. Every word adds value, making it a model of concise, structured documentation.

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 that there are no parameters, an output schema exists (so return values are covered), and annotations already declare readOnlyHint, the description completes the picture by defining the data scope and usage boundaries. Nothing an agent needs to decide whether to call this tool is missing: it knows what it does, what data it excludes, and when not to use it.

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 has zero parameters, so schema coverage is 100% (vacuously). With no parameters to document, the baseline for parameter semantics is 4, and the description doesn't need to add anything. The description introduces no parameter-related info, which is appropriate because there are no parameters to explain.

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 clearly states the verb 'Read' and the specific resource: 'aggregate public statistics from the AOPX production pilot'. It enumerates what is included (consultation and reported-outcome activity) and explicitly excludes LAB benchmarks, tests, shadow runs, and replay events, making the scope unambiguous. It also differentiates from siblings by stating it is for public telemetry, not provider recommendation, distinguishing it from recommend_provider.

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?

The description provides explicit usage guidance: 'Use this for public telemetry, not for provider recommendation.' This tells the agent when to use it and what it is not for. It also adds context by listing excluded data categories, which helps avoid misuse. While it does not name alternative tools explicitly, the clear exclusion of provider recommendation implies a different tool for that purpose, so guidance is strong but not exhaustive.

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