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

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false, so the safety profile is covered. The description adds genuinely new operational detail the annotations cannot convey: it spends account balance, is billed per input token only, that usage.cost_rub reports the charge, and that all questions are answered in a single call. That billing transparency is exactly the extra context a paid, non-idempotent call needs.

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 with the core purpose in the first sentence, then question-type semantics, then usage guidance, then cost. Four short paragraphs, each with a distinct job; no filler or repetition of the name/title. 'Same as POST /v1/decisions' is minor API-reference detail but plausibly useful to the caller.

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 carries the return burden and does explain the cost field (usage.cost_rub) and that each question key comes back in answers. Combined with annotations covering safety and full schema coverage, an agent has enough to call it correctly. It could say more about failure modes or invalid model ids, which is why it is not a 5.

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 already documents model, state and questions. The description still adds meaning beyond it by defining each question type semantically — noul is 'yes or no, answered as the probability of yes' — which the schema only gestures at via an example. It adds nothing further for model or state, so it does not reach 5.

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 (ask) and resource (a decision model, with named examples Jev/Clef/Mercury Decide) and immediately draws the defining contrast: 'get probabilities instead of text.' It distinguishes itself from the closest sibling, chat_complete, by output type rather than leaving the agent to infer.

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?

'Good for classification, routing and triage where you need a calibrated number rather than prose — and much cheaper than asking a chat model' gives clear when-to-use context and implicitly steers away from chat_complete. It stops short of an explicit 'use chat_complete instead for X' rule or any when-not-to-use exclusion, so it is strong but not complete.

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