jev_ask

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

With no annotations, the description carries the full burden and adds two genuinely useful disclosures: confidence is distribution concentration not correctness, and the default cost uses a changeable early-access price. These are valuable caveats, but core behavioral aspects — how the tool responds to invalid questions, determinism, error behavior — are left unstated.

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, each earning its place: purpose plus exclusion first, then two high-value caveats. Front-loaded and free of repetition or padding. This is a model of efficient description writing.

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?

The output schema covers return values, and the discriminated-union schema structure carries some explanatory weight. But for a tool with 0% parameter documentation, no annotations, and a complex nested schema, the description leaves meaningful gaps — particularly around what constitutes a valid state and when to use each question type. Adequate but not complete for the complexity level.

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 0%, so the description must compensate. It only loosely maps 'supplied state' to the state parameter and 'typed judgments' to questions, adding little real semantic meaning. The nested questions schema with three discriminated types (noul, choice, score) is structurally self-documenting but semantically unexplained; the description leaves the agent to infer what each question type means.

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 states a specific verb and resource ('typed judgments over supplied state') with an explicit scope exclusion ('not for text generation or numeric extraction'). This distinguishes it from more specific sibling tools like jev_classify or jev_score, though it doesn't name them directly, so it falls short of a 5.

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 negative clause 'it is not for text generation or numeric extraction' provides a when-not-to-use signal, and 'typed judgments' implies when to use it. However, no sibling alternative is named and no explicit condition for choosing this tool over jev_classify, jev_score, etc. is given. Usage context is implied rather than stated.

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