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

Beyond the readOnlyHint and openWorldHint annotations, the description discloses rich behavioral traits: independence semantics ('no question can use another's answer'), the meaning of confidence ('says how concentrated the distribution is, not how likely the answer is to be correct'), and input constraints ('a URL here is not a request to browse'; 'accepts no image, audio, or file uploads'). No contradiction with annotations — readOnlyHint aligns with the judgment-returning nature.

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 long but every section earns its place: three paragraphs cover call design, question-type selection, and result interpretation, with scannable headings. It is front-loaded with the core purpose and the independence rule appears early. For a tool with three question types, nested objects, and confidence semantics, this density is justified; only slight tightening would make it a 5.

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?

For a high-complexity tool, nothing needed for correct invocation is missing: independence rules, second-call conditions, type selection, confidence calibration warnings, and input limitations are all covered. The output schema exists and the description still explains the answer shape ('answers keyed by question id') and the absence of a confidence field on noul. An agent can call this safely and interpret results correctly without further clarification.

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 3, but the description adds interplay semantics the schema cannot capture: 'state carries the text and facts; instructions carries the judgment, in the question's own words.' It also adds the dotted-path referencing rule ('account.plan') and maps the type enum to concrete use cases. This exceeds the baseline yet stops short of exhaustive — the schema already documents each parameter well.

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 opening sentence is explicit and complete: 'Send one shared context and one or more independent questions to Jev (TypeSafe System One) and get typed judgments back — not prose.' It names the verb, resource, batch scope, and output format in one sentence. The batch nature ('one or more independent questions') clearly distinguishes it from the single-type siblings jev_choice, jev_score, and jev_noul.

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 routing and design rules: 'Ask independent questions together in one call... make a second call only when an answer decides what to fetch or ask next.' It also prescribes type selection with an edge case: 'Prefer choice for a category, score for an ordered scale, and noul for a yes/no judgment. Use separate noul questions when several labels can apply at once.' This is when-to-use guidance that leaves nothing to inference.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description goes beyond by detailing the return value ('the selected option, the full probability distribution, and `confidence`') and crucially explains the semantics of `confidence`: it is 'not the probability that the selection is correct' but a measure of distribution concentration, with actionable advice to compare against a calibrated threshold and escalate low-confidence results to a human. This is rich behavioral context not present in annotations.

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 a single tight paragraph of about 150 words. It opens with the core purpose, then delivers usage rules, option structuring, and return/confidence handling in logical order. Every sentence earns its place—no redundancy or filler. It is front-loaded with the most critical info and remains skimmable.

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?

For a tool with 5 parameters (3 required) and a complex judgment task, the description covers the essential aspects: how to define options with boundaries, when to include 'other'/'none', the instruction form, and how to interpret the output. It does leave out explicit error cases or edge conditions, but the output schema (present per context) likely fills that gap. Given the richness already present, the description is quite complete.

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 3. The description adds meaningful semantics beyond the schema: for `criteria` it explains the object form (each option maps to a description of what it covers/excludes) and the array shorthand, plus the 'other'/'none' guidance; for `instructions` it says to write the judgment in full; for `state` it warns that a URL is not a browse request and clarifies no file uploads. These enrich the parameter understanding and compensate for the generic schema descriptions.

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 precise verb-resource-action: 'Ask one question whose answer is exactly one of a set of options you define.' It immediately scopes the tool to category/route/closed-list selection and explicitly excludes degree and yes/no uses, distinguishing it from siblings like jev_score and jev_noul. The statement is unambiguous and tied to a specific resource and constraint.

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 when-to-use guidance: 'Use for a category, a route, or a selection from a closed list — not for a degree and not for a yes/no.' It also names the sibling for multi-label cases: 'ask separate noul questions (together in `jev_ask`) when several labels can apply at once.' Additionally, it advises including 'other' or 'none' for open lists, aligning with the openWorldHint. This provides clear routing and alternatives.

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

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

Beyond the annotations, the description explains the output scale (0-1, near 1 yes, near 0 no, near 0.5 uncertain), clarifies that it measures probability not intensity, and warns there is no confidence field so low values mean evidence against. It also notes practical constraints like no image/file uploads and that a URL is not a browse request.

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 well organized with a clear opening, usage condition, optional parameter guidance, and output interpretation. Every sentence adds value; there is no repetition of schema boilerplate or filler.

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?

For a tool with nested objects, multiple optional parameters, and an output schema, the description is remarkably complete. It covers the judgment semantics, parameter usage, output interpretation, and key behavioral constraints, leaving little for an agent to infer on its own.

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%, but the description still adds substantial meaning: how to reference nested fields with dotted paths in backticks, that a URL is not a request to browse, that question_id is not sent to the model, and that criteria can be omitted when instructions already define both outcomes. This goes well beyond the schema.

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 operation: 'Ask one yes/no question and get its probability.' It names the resource and the output, and makes the yes/no scope explicit. It does not explicitly differentiate from the sibling tools by name, though the probability focus distinguishes it from category-based alternatives.

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 opens with an explicit 'Use when' rule: the condition either holds or does not, and probability is more useful than a category. This gives clear context for selecting the tool, though it does not explicitly name sibling alternatives or state when not to use them.

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

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

Annotations already mark it read-only and open-world; the description adds substantial operational detail: scores are probability-weighted means of level numbers, not percentages or category IDs, fractional values fall between levels, and identical scores can arise from different distributions, so `probabilities` must be read when that distinction matters. It also discloses the output fields.

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?

Every sentence carries distinct value: purpose, usage scope, criteria construction rules, output contract, and an interpretation caveat. It is front-loaded with the core action and remains efficient despite its length.

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?

For a tool with 100% schema coverage, a rich output schema, and read-only annotations, the description is fully sufficient. It covers how to phrase criteria, how scoring works, what the response contains, and a pitfall about equal scores from different distributions.

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 3. The description adds meaning beyond the schema by explaining that `criteria` levels are ordinal and scored 0..n-1 with fractional positions, and that the score is a probability-weighted mean rather than a category identifier, which is essential for correct usage.

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 first sentence states a precise action: 'Ask one question whose answer is a position on an ordered scale you define.' This clearly identifies the tool as an ordinal/scalar judgment maker and differentiates it from generic ask tools by concept, though it does not explicitly name or contrast the sibling tools.

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 an explicit usage context: 'Use for degree, severity, or intensity.' It also warns against relative criteria and explains when to inspect probabilities. However, it does not state when not to use this tool or name alternatives among siblings.

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