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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by specifying the statistical metrics returned (mean, median, std deviation, winner, improvement percentage), which goes beyond the structured annotations. No contradictions.

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 a single, well-structured sentence that efficiently covers purpose, input, and output without fluff. It is front-loaded with the primary action 'Generate an A/B test report'.

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 presence of an output schema and simple input schema, the description adequately covers what the tool does. It could mention edge cases like equal scores or unequal array lengths, but for a straightforward statistical tool, it is sufficiently 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?

Schema coverage is 100%, but the description adds semantic meaning by calling the variants 'prompts or model configurations', which is not explicit in the schema's generic 'variant configuration'. It also clarifies that 'scores' are arrays for statistical comparison, aiding understanding.

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?

Description clearly states the tool's function: 'Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison' with specific outputs. This distinguishes it from sibling tools like compare_models or compare_responses, which likely have different comparison methods.

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 clearly implies when to use: when you have two sets of scores to compare statistically. It mentions 'two prompts or model configurations' and 'arrays of scores', which is sufficient context. It doesn't explicitly name alternatives, but the usage context is clear.

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