Ask Jev for typed decisions

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

Even though annotations already declare readOnlyHint=true and destructiveHint=false, the description adds valuable behavioral context: question IDs are not sent to the model, the call sends supplied state to TypeSafe, may incur API charges, and does not execute selected actions. It also discloses what the returns contain (raw judgments, probabilities, confidence, token usage). No contradiction with annotations 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?

The description is dense but every sentence earns its place: type semantics, batching guidance, parameter intent, behavioral caveats, returns, and cost/non-execution notes are all present. It is front-loaded with the core purpose and then progressively adds usage and safety detail. There is no filler or tautology.

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 the complex nested schema and the presence of an output schema, the description covers everything an agent needs: how to define each question type, what to put in state, which constraints apply (independence, other/none choice), what the call returns, and that it does not execute actions. The absence of sibling tools removes the need for differentiation, and return-value details are covered by the output schema. Nothing critical is missing.

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 baseline is already strong, but the description adds meaning beyond the schema. It explains that state should contain evidence, definitions, and policies; that questions are named and independent; and that noul returns probability of yes rather than intensity. It also clarifies that 'score returns a position on 2–10 ordered levels (0-based)', which the schema alone does not convey. This materially helps an agent construct valid inputs.

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?

The description opens with a specific verb-plus-resource ('Evaluate supplied context using TypeSafe Jev') and then enumerates the three decision types (choice, score, noul) with their distinct semantics. It clearly differentiates the tool's function from any generic decision helper, and the title aligns with the behavior. No ambiguity remains about what the tool does.

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 gives explicit usage guidance: batch independent, narrow questions in one call, include evidence and full instructions, add an other/none choice when appropriate, and use caller-defined policies for uncertainty. It also states an exclusion—questions cannot use each other's answers—which tells an agent when not to combine or chain questions. This is actionable and specific.

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