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

Annotations already cover read-only and open-world traits, and the description adds return shape (`answer.choice`, `answer.probabilities`, `answer.confidence`), a hard limit of 255 options, image size/format limits of 4 images, PNG/JPEG/WebP, 4 MiB each, and the model-capability rejection behavior. It doesn't describe failure modes for oversized criteria or missing model access, but the constraints it does disclose go well beyond the 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 purpose and the return values are front-loaded, then parameters follow in a logical order. It is dense and packs image-handling detail into one long sentence, but essentially every clause — limits, path constraints, model capability — is load-bearing rather than 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 6-parameter tool with nested objects and an output schema, the description covers what the output schema cannot say on its own: the option cap, the 'none' pattern, the image rules and the single-question scope. Nothing an agent needs to invoke it correctly 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?

With 0% schema description coverage the description must carry the whole burden, and it does: `criteria` is explained as a name-to-description map (or null), `state` as string or structured JSON, `instructions` as the full question with backticked paths like `ticket.messages[0].text`, and `images` entries as either a path inside JEV_IMAGE_DIRS or a base64 data URL.

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 states a specific verb and resource: 'Ask TypeSafe Jev to pick one option for some state', which tells an agent exactly what the tool produces. It is clearly separable from jev.evaluate, the multi-question sibling, which is named later in the text.

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?

Explicit routing rules are given: 'include a "none" option when nothing may fit' and 'To ask several questions about the same state, use jev.evaluate instead'. It also states that only image-capable models accept images and that other models reject them, so the agent knows when this call should not be made.

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?

Goes well beyond readOnlyHint/openWorldHint: questions run in parallel against the same state and cannot see each other's answers, provider is restricted to "typesafe", and images must live under JEV_IMAGE_DIRS or be inline data URLs with a 4 MiB cap and image-capable-model requirement. Those are exactly the behavioral facts an agent would otherwise discover by failing.

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-loads the core purpose, then uses a bulleted question-type block, then tacks on optional concerns (images, provider). Dense and mostly waste-free, though the question-type bullets partially duplicate what the output schema already declares.

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 5-parameter tool with deeply nested question objects, the description covers the state/questions interaction, per-type criteria contracts, model/image constraints, and even summarizes return fields despite an output schema existing. An agent has everything needed to construct a valid call on the first try.

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 carries the full load and does so thoroughly: `state` as string or JSON, `questions` as an ID-keyed map with the three variants and their criteria shapes and limits (255 options, 2-10 levels), `images` formats and sources, `model` default, and `provider` restriction. Little about the parameters is left ambiguous.

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 and resource: ask typed questions about a piece of state, get calibrated probabilities back. That is far more precise than a name restatement. It does not, however, differentiate itself from siblings jev.noul, jev.choice, jev.score or jev.evaluate_batch, so an agent must infer that this is the multi-question, multi-type entry point.

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?

Offers concrete operating guidance: one narrow judgment per question, question IDs are invisible to the model so `instructions` must be self-contained, backticked paths for structured state, never place images inside `state`, and images require an image-capable model. It stops short of saying when to prefer this over jev.noul / jev.choice / jev.score / jev.evaluate_batch.

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?

Goes well beyond readOnlyHint/openWorldHint: discloses per-item billing, all-or-nothing pre-validation ('one invalid item rejects the whole call'), per-item ok/error/skipped statuses with the auth/no-credits cascade rule, and image model capability limits. This is exactly the operational context an agent cannot infer from 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?

Front-loaded with purpose and usage patterns, then parameters, then provider. Dense but nearly every sentence adds a constraint; the paragraph on image models and the JEV_IMAGE_DIRS aside could be tightened slightly.

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?

Output schema exists, yet the description still summarizes `results`/`summary`/`usage` shapes in a way that clarifies ordering and status semantics. Together with the image, validation, and billing rules, an agent has everything needed to call this correctly on the first try.

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 carries the full burden and does so: it documents the `items` element shape, the optional `id`, the two image forms with size/format caps and the JEV_IMAGE_DIRS restriction, that `provider` only accepts 'typesafe', and that images must never be nested in `state`. These are non-obvious constraints absent from 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?

States a specific verb and resource — 'Ask TypeSafe Jev the same typed questions about many items (up to 100) in one call' — and names the motivating use cases (classify tickets, score and sort search results). The batch-vs-single distinction from jev.evaluate is made explicit via the reference that questions/model 'work exactly as in jev.evaluate'.

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?

Gives concrete routing advice: use a choice question to classify, a score or noul question to rank, several questions to tag, and points to jev.evaluate for parameter semantics. It doesn't state an explicit exclusion (e.g. 'use jev.evaluate for a single item'), but the one-call/up-to-100 framing makes the boundary obvious.

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 and openWorldHint, so the description adds real value beyond them: it discloses partial-failure behavior — an unreachable provider surfaces in `errors` rather than failing the whole call. It stops short of describing ordering, pagination, or size of the result set.

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 output shape and filtering behavior are front-loaded and every clause carries information. There is mild redundancy between "Pass `provider` to list one provider only" and "`provider` is optional", which keeps it from 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?

With an output schema present, the description needn't enumerate return fields, and it still calls out the key edge case (`errors`). For a zero-required-parameter read tool this is close to complete; only result-volume or sorting behavior is unaddressed.

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 0%, so the description must carry the meaning of `provider`. It does: the parameter is optional, narrows output to a single provider, and the only valid value on this server is "typesafe" — enough for an agent to supply or omit it correctly.

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 gives a specific verb and resource: list model names and aliases per provider, tagged with `provider`, that can be used in the `model` field. It is clearly distinct from the sibling evaluation tools (jev.evaluate, jev.score) by resource, though it never names a sibling explicitly.

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?

It tells the agent when to pass `provider` (to list one provider only) and that omitting it is fine because the server offers only "typesafe". There are no stated exclusions or named alternatives, but for a single-parameter list tool the usage context is clear.

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 only say readOnlyHint/openWorldHint; the description adds substantial context beyond them: the 0..1 probability scale and the trap reading of 0.5, the 4-image / 4 MiB / PNG-JPEG-WebP limits, the JEV_IMAGE_DIRS sandbox for path inputs, the base64 data-URL form, the warning never to put images inside `state`, and model capability rejection behavior.

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 purpose and return semantics before parameter detail, and nearly every sentence carries non-obvious information. It is a dense block rather than a scannable structure, and the image/model constraints bleed into what would otherwise be a clean parameter walkthrough.

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?

An output schema exists, so the return value needn't be re-explained, and annotations cover the safety profile; the description fills the real gaps (image sandboxing, model capability, state/instructions discipline). The only missing piece for correct invocation is guidance on choosing `model` rather than leaving it as a bare 'optional'.

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 0%, so the description must carry the load, and it mostly does: `state` (string or JSON object/array), `instructions` (must state the full question, reference structured state via backticked paths like `ticket.messages[0].text`), `criteria` {true,false}, `images` (both shapes with limits), and `provider`. The gap is `model`, which is only called 'optional' with no guidance on valid values or how to discover them (jev.models exists), which matters given the image-capability constraint.

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 and resource ('Ask TypeSafe Jev one yes/no question about some state') and immediately defines the return semantics as `answer.noul`, a probability 0..1 with 0.5 = equally likely. It also distinguishes itself from the sibling `jev.evaluate` by scoping to a single question, so an agent can pick between them without opening the schema.

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?

Gives an explicit routing rule: 'To ask several questions about the same state, use jev.evaluate instead: it answers them in one request.' It also states when-not conditions for images (only image-capable models, names concrete examples) and notes the server only offers provider 'typesafe', leaving no ambiguity about 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?

Annotations only declare readOnlyHint and openWorldHint, but the description adds real behavioral context: image constraints (max 4, PNG/JPEG/WebP, 4 MiB each), the JEV_IMAGE_DIRS sandbox for path-based images, the fact that only image-capable models accept images and others reject them, and that the only supported provider is 'typesafe'. It even discloses the return shape, including that scores are probability-weighted and may fall between levels.

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 operation and return shape, then parameter guidance, then the sibling routing hint — a sensible order. It is dense and a few clauses (base64 data URL format, per-format repetition of PNG/JPEG/WebP) duplicate what the schema already documents, so it is somewhat longer than strictly necessary.

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?

An output schema exists, yet the description still names the key return fields (answer.score, answer.legend, answer.probabilities, answer.confidence), which helps an agent interpret the probability-weighted index. Combined with the model/provider caveats and image rules, nothing essential for a correct invocation 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 description coverage is 0%, but the description compensates for every parameter: `state` (string or JSON object/array), `instructions` (must state the full question, use backticked paths into structured state), `criteria` (2-10 ordered levels), `model` and `provider` (both optional, provider restricted to 'typesafe'), and both image forms with their required keys. It also gives a concrete path syntax example, which the schema does not.

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 and resource — rate a state along ordered levels — and immediately pins down what `criteria` means (2-10 level descriptions, lowest first). It also names the sibling it is not for the multi-question case, so an agent can distinguish it from jev.evaluate without opening either schema.

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?

Explicit routing guidance: 'To ask several questions about the same state, use jev.evaluate instead' gives a clear when-to-use-the-alternative rule, and the image/model compatibility note warns when this tool cannot be used. It does not address jev.choice or jev.evaluate_batch, which are plausible nearer alternatives for single-label judgement or multiple states.

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