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

Annotations only declare it is not read-only, not destructive, and closed-world, so the description earns credit for adding the actual lifecycle: the variant arm is delivered to the SDK, quality parity gates the decision, and promotion results in a verified model swap on fresh traffic. It does not say whether a test can be started twice concurrently or what happens to an existing test, but the side effects it does disclose are substantive and consistent with destructiveHint=false.

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?

Three dense sentences, front-loaded with the purpose and scope before moving to SDK prerequisites and promotion behavior. The SDK-configuration sentence is information-dense and slightly syntax-heavy, but each sentence carries non-redundant information and none is filler.

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?

For a mutation tool with no output schema and annotations covering only safety hints, the description supplies the missing conceptual model: what varies, what gates promotion, and what integration is required. It does not describe the created object or how to observe the test afterward, though get_production_test and get_model_optimization_status siblings partially cover that.

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%, so parameter meanings are already documented, giving a baseline of 3. The description still adds real semantics for targetModel by explaining that it must be served through SDK configuration (same-client override or resolveModel hook) rather than merely passed in, which is not in the schema.

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 specific verb and resource (start a production A/B test) and immediately narrows the scope to model-only changes, stating that the instruction stays fixed while a second model serves the variant. Combined with 'No optimization is required,' this implicitly separates it from optimize_model, trigger_optimization, benchmark_models, and start_production_test well enough that an agent can route correctly.

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 clear usage context (production A/B test where only the model varies, cheaper model ships only if quality holds) and states a precondition for success: the SDK must be configured to serve the target model via a same-client override or resolveModel hook. It never explicitly names alternatives such as benchmark_models for offline comparison or the prompt-variant production test tool, so the when-not-to-use side is left to inference.

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