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

Annotations cover the read-only/idempotency profile, and the description adds valuable context beyond them: live prices, output units (USD per call and per 1,000 calls), a paid-call price ($0.01 USDC via x402/prepaid) and a free-trial flag. It does not, however, explain ranking direction or the fallback behaviour when no suitable model exists.

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?

Dense but front-loaded: opening sentence states the core promise, then inputs, then outputs, then billing. Every sentence carries information; the only mild bloat is repeating the price twice (per-call and free-trial mention in one clause).

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 nine optional parameters, an output schema (so return values need no description) and rich input schema, the description is appropriately complete: it covers inputs, cost outputs and the payment channel. It leaves minor gaps — ranking behaviour and handling of no-match results — but nothing that blocks correct invocation.

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 the schema documents all nine parameters with defaults, maxes and enums. The description reinforces the intent of the task/quality inputs but adds no syntax or format detail beyond what the schema already provides (e.g. it doesn't specify tie-breaking or how the AI infers features from a task string).

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 (pick) and resource (cheapest suitable AI model), explains exactly what the agent gets back (cheapest suitable models across major providers with per-call and per-1k-call cost), and its primary differentiator vs the rest of the catalog is the cost-optimisation objective.

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 context on when to use it — you need to pick a model for a task and want the cheapest one — and offers two input styles (describe a task, or enumerate features). It does not, however, name a sibling alternative (e.g. ai-models) or state when NOT to use it.

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