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

Annotations only say it is not read-only, not destructive and not open-world. The description goes well beyond that by disclosing the async job pattern (immediate jobId), the execution cost profile (baseline once + every challenger), and the statistical method (3 runs per scenario, median scoring), which materially affects how an agent should budget and follow up. It does not state auth/scoping requirements, e.g. the superadmin-only customerId path.

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 sentences, front-loaded with what the tool does, then how it runs, then the async handoff. Every sentence carries distinct information and none restates the name or annotations.

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 correctly covers the immediate return (jobId) and the polling path, and it hints that goal affects winner selection. It does not describe the shape of the per-scenario delta results the agent will eventually retrieve, but the get_benchmark_status pointer largely compensates.

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 every parameter including the baseline/challenger ordering, goal enum, and fineTunedModels pricing fields is already documented. The description reinforces the baseline-vs-challenger concept and the default run count but adds no syntax or format beyond the schema; baseline 3 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?

States a specific verb and resource ('Compare multiple LLM models on the same scenarios for an agent') plus the mechanism (baseline run once, then each challenger, per-scenario deltas). An agent can distinguish this tuning/benchmark job from siblings like start_model_test or simulate_ab_test 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?

Provides clear operational context: it returns a jobId immediately and directs the caller to get_benchmark_status for polling, which is exactly the when/what-next guidance an agent needs for an async tool. It stops short of naming when to prefer this over adjacent tools such as optimize_model or simulate_ab_test, so no explicit exclusions or alternatives are given.

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