Ask Jev many typed questions at once

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds valuable behavioral context beyond that: 'every question is evaluated against the state in parallel, so extra questions cost a few tokens and almost no latency' reveals efficiency behavior, and 'Jev decides, it does not generate' plus the explicit list of non-capabilities (summarize, translate, write code, explain reasoning) defines the model's behavioral boundaries. This significantly exceeds what annotations provide.

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 compact (around 120 words) and every sentence earns its place. It front-loads the core purpose in the first sentence, then layers on parallel evaluation efficiency, usage preference, behavioral constraints, and the read-only guarantee. There is no redundancy or fluff; the structure flows logically from what the tool does to how to use it to what it cannot do.

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 tool is complex (8 parameters, nested question objects, no output schema), and the description covers the essential usage guidance and behavioral constraints. It does not explain the response structure, but the schema richly documents the response-affecting parameters (raw, include_usage, include_probabilities, confidence_threshold). Since annotations cover safety and the schema covers parameters, the description is largely complete, though a brief note on response shape (e.g., 'returns an object keyed by question name') would elevate it. Given the schema's richness, a 4 is appropriate.

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 100%, so the baseline is 3. The description adds high-level guidance about the 'questions' parameter by stating 'every question needs a defined answer space (a label set, a rubric, yes/no),' which complements the schema's detailed per-type definitions. However, it does not describe the response-format parameters (raw, include_usage, include_probabilities, etc.) or the confidence_threshold behavior, leaving those to the schema. The added context is helpful but not comprehensive, hence a 4.

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 clearly states the tool's function: 'Ask Jev one or more typed questions about a state and get calibrated decisions back.' It uses a specific verb (ask) and resource (Jev), and explicitly distinguishes itself from sibling single-question tools by emphasizing 'mixed question types share one request' and 'prefer one call with many questions over many calls.' It also clarifies what the tool does not do (cannot summarize, translate, write code, or explain reasoning), leaving no ambiguity about its scope.

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: 'Prefer one call with many questions over many calls,' directly steering agents toward this tool for batched questions and away from multiple single-question calls. It also states the requirement that 'every question needs a defined answer space' and lists exclusions (cannot summarize, translate, etc.), which implicitly guides when NOT to use it. This is concrete, actionable guidance that distinguishes it from siblings like jev_score or jev_choice.

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