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

Annotations only declare readOnlyHint=false and destructiveHint=false; the description adds meaningful behavior beyond that: asynchronous execution with an immediate jobId return, repeated runs per scenario, and median-based aggregation. It does not mention permission requirements or expected runtime, which leaves a modest gap for a job-creating tool.

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 tight sentences with the core action front-loaded, then the mechanics, then the follow-up call the agent needs. No filler or redundancy.

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?

With no output schema, the description helpfully states the immediate return value (jobId) and the polling tool, which is the key thing an agent must know for a long-running job. It stops short of covering failure modes or estimated duration, so it is strong but not exhaustive.

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 the schema already documents every parameter including baseline vs. challenger ordering, goal enum, and per-scenario run counts. The description restates the baseline/challenger concept and the 3-runs/median default, adding only marginal value beyond the structured field.

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 — comparing multiple LLM models on the same agent scenarios — and clarifies the baseline-then-challenger structure, which distinguishes it from siblings like start_model_test or simulate_ab_test. An agent can tell what it produces (per-scenario deltas) without opening the 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?

Gives clear operational context: it runs a job and returns a jobId immediately, so the agent must poll with get_benchmark_status. It does not, however, say when to prefer this over adjacent siblings like start_model_test, regression_test, or simulate_ab_test, so the selection guidance is incomplete.

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