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

Annotations only declare readOnlyHint=false, destructiveHint=false, openWorldHint=false, so the description carries real weight and adds value: pass/fail per scenario, fluke detection for suspected regressions, automatic golden-suite generation (explaining why readOnlyHint is false), and superadmin-only auth for customerId. It does not disclose whether execution is synchronous or asynchronous, which is a gap given sibling polling tools like get_benchmark_status and get_simulation_status.

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?

Front-loaded purpose, then behavior, then the variant-specification options — a logical order with no filler sentences. It is slightly longer than needed because the numbered variant section restates details already present in the schema.

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?

There is no output schema, and the description compensates by describing the return shape (pass/fail per scenario plus fluke detection). It also covers the auto-generation fallback and auth requirements. The one omission is execution model — sync vs async/polling — which matters given the number of status-polling siblings.

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 100%, so all five parameters are already documented in the schema, including the 'Use this OR...' exclusivity and the override find/replace semantics. The description largely recaps that structured information rather than adding new meaning, which is the baseline 3 case when the schema does the heavy lifting.

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?

States a specific verb and resource: 'Run regression test for a prompt variant against its golden test suite,' with the scope of the test (pre-validated scenarios, known success criteria) spelled out. An agent can distinguish this from sibling tools like validate_variant or simulate_agent without opening a schema.

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 explains the three mutually exclusive ways to supply a variant (variantPrompt, variantPromptId, variantOverrides) and notes the auto-generation fallback when no golden suite exists, which is clear operational context. It never names an alternative tool or states when this should be chosen over validate_variant or simulate_agent, so no explicit exclusions.

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