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

There are no annotations, so the description carries the behavioral burden. It adds meaningful context by specifying that LAB benchmarks, tests, shadow runs, and replay events are excluded, making clear the tool returns only production aggregate data. Given the output schema exists, the lack of return-format details is acceptable.

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?

Two concise sentences with the main purpose front-loaded and the data-scope exclusions clearly stated. No filler or redundant phrasing.

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?

This is a parameterless, read-only statistics tool with an output schema available, so the description only needs to convey what data is included and excluded, which it does. The exclusion sentence adds essential scope context, making the definition complete for successful invocation.

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 and the input schema is empty with 100% coverage. There is nothing for the description to add regarding parameter meanings, so the baseline 4 for no-parameter tools applies.

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 uses a specific verb ('Return') and identifies the exact resource: aggregate AOPX production consultation/outcome statistics. It clearly distinguishes itself from siblings like public_outcomes and report_outcome by emphasizing aggregate stats rather than individual outcomes or reporting actions.

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 implies when to use the tool by saying it returns production statistics and excluding non-production data, but it does not explicitly mention alternatives or provide when-not-to-use guidance. An agent can infer usage context, but the routing is not explicit.

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