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

With no annotations, the description carries the burden and does well: it discloses the always-returns-a-box behavior, the required model version and default override, the image-in-request requirement, and the answer key/return shape. It omits any mention of permissions, latency, or failure modes when the image is missing.

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 core capability and return format are front-loaded in the first two sentences, with the model-default caveat and the jev_noul pairing placed after. It is a touch dense with parentheticals but every sentence carries actionable information.

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-param, nested-object tool with no output schema, the description supplies the missing return-value detail (top/bottom/left/right as 0..1 fractions, confidence, under "answer"), the image-in-request requirement, and the model-version constraint. Nothing an agent needs to call it correctly is absent.

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 value beyond the schema by explaining *why* model defaults to jev-1.14.0 ("Box needs Jev 1.14") and by clarifying that the image is referenced from state/instructions and passed via assets. That reasoning is not derivable from the schema alone.

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 — "locate one target in an image" and return box edges — and distinguishes the tool from the sibling `jev_noul` by framing Box as a localization tool versus a presence check. An agent can tell it apart from `jev_decide`/`jev_score`/`jev_noul` without opening any 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?

It gives an explicit selection rule: "pair it with jev_noul when you need a presence check," plus the caveat that a Box answer is always a box even when the target is absent. It doesn't explicitly contrast with jev_decide/jev_choice/jev_score, so the routing across the full sibling set is incomplete.

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?

With no annotations, the description carries the behavioral-disclosure burden. It usefully states the return shape (chosen option, confidence, calibrated probabilities, and answer under the key "answer"), but does not discuss side effects, permissions, latency, cost, or failure 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?

The description is three tight sentences with no wasted language. Purpose is front-loaded, followed by return behavior and a key output detail.

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 description covers the core purpose and return shape, and the schema fully documents all six parameters. Given the absence of annotations and output schema, it is substantially complete, though it could say more about operational constraints or alternatives.

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 parameter meaning is already well documented in the schema. The description reinforces that options come from a labelled set and that the answer is keyed as "answer", but adds little else beyond the structured parameter 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 states a specific operation: asking Jev a single-choice question and picking exactly one option from a labelled set. It clearly defines the resource and constraint, but does not explicitly contrast this with sibling tools such as jev_decide or jev_score.

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 implies the tool is for single-choice selection from labelled options, which gives some usage context. However, it does not say when to use this instead of jev_decide or jev_score, nor does it state exclusions or 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?

With no annotations, the description must carry behavioral disclosure; it does say the call returns typed answers with calibrated probabilities and accepts text or structured state, which is more than nothing. It says nothing about latency/cost, batching limits, determinism, or failure modes, and leaves long-context behavior to the schema.

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 compact sentences, front-loaded with what the tool does and what it returns, then the image caveat. No filler, though the image sentence is somewhat dense and could be trimmed since the schema repeats it.

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-param nested tool with no output schema, the description covers purpose, accepted state formats, the four question types, and the general return shape (typed answers with calibrated probabilities). Missing detail on the answer payload structure and multi-question semantics is partly mitigated by the exhaustive schema text.

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 schema already documents all five parameters. The description's image note (pass in `assets` and reference each image in state/instructions/criteria) largely restates what the `assets` schema description says, adding little beyond the structured field. Baseline 3 applies.

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 the Jev decision model typed questions) and enumerates the question types (choice, score, noul, box), which implicitly maps to the sibling tools. It signals batching ('one or more ... in a single call') without naming jev_choice/jev_score/etc. as alternatives, so differentiation is present but inferential.

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 phrase 'one or more typed questions ... in a single call' hints that this is the multi-question entry point, and the image guidance covers one specific case. But there is no explicit 'use this instead of jev_choice when...' routing or statement of when a single-type sibling is preferable.

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?

No annotations exist, so the description carries the burden. It correctly signals a read-only enumeration scoped to the API key and discloses the returned fields, but says nothing about failure modes (invalid/missing key), result counts, pagination, or ordering.

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?

A single sentence with the operation and scope front-loaded and the return fields trailing as a parenthetical. Nothing is padded or repeated.

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 simple, parameterless, and has no output schema, so the description covering the resource, the key scoping, and the fields returned is close to sufficient. Only the absence of any error/empty-result behavior keeps it from being fully 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?

The tool takes zero parameters, so there is nothing to disambiguate and the baseline of 4 applies. The parenthetical list of returned fields is a small bonus but is return-value information rather than parameter semantics.

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 (List) and resource (TypeSafe AI models) plus the scoping condition (available to the configured API key) and enumerates the returned fields. The sibling names (jev_decide, jev_choice, jev_score, jev_noul, jev_box) are opaque and share no resource domain, so no sibling differentiation is possible or needed here.

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?

Usage is implied by 'available to the configured API key' — an agent can infer this is a discovery/introspection call — but the description never states when to call it (e.g. before invoking a model) or names any alternative. No exclusions or prerequisites are given.

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?

No annotations are provided, so the description carries the burden alone. It usefully discloses the return shape (a single calibrated probability under the key 'answer'), which is real behavioral value. However it says nothing about cost, latency, failure modes, or how the optional criteria/assets/long_context alter the estimate — notable gaps for an un-annotated inference call.

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?

Two sentences, front-loaded with the verb and resource, then the return contract. Nothing wraps around or repeats the name; every clause earns its place.

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?

It covers the core call and its return value adequately for a tool with no output schema, but with six parameters including nested objects (assets, criteria) and no annotations at all, the description leaves the optional parameters' effect on the estimate unexplained and gives no guidance on choosing among the several jev_* siblings.

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 baseline would be 3, and the description genuinely adds semantics by linking the two required parameters: the proposition in 'instructions' is evaluated against 'state'. That relational meaning is not spelled out in the schema descriptions, which treat each parameter separately.

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 Jev a single Noul question' — and pins down the operation as estimating a probability in [0,1] that a proposition about the state is true. It also names the return key, so the agent knows exactly what comes back. It stops short of naming how it differs from siblings like jev_decide or jev_score, which is why it isn't 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?

Usage is only implied: the binary true/false probability framing suggests when this fits, but there is no explicit 'use this instead of jev_choice/jev_score when...' statement and no prerequisites or exclusion cases. An agent must infer the boundary from the sibling names alone.

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?

With no annotations and no output schema, the description carries the burden well by disclosing the return payload (numeric score, confidence, per-level probabilities) and the key under which the answer is nested ('answer'). It omits cost/latency/limits behavior and doesn't state that only one question per call is processed beyond the word 'single'.

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 tight sentences, front-loaded with the action and rating semantics, then the return shape. Every sentence carries information an agent needs; nothing is padding.

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 no output schema, the description covers what the tool does and what comes back, which is the main gap the schema leaves. It could say more about the levels ordering constraint interacting with outputs, but nothing essential for 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 100%, so model, assets, levels, instructions, and long_context are already documented in the schema. The description only restates that the scale is ordered and defined by the caller, adding little beyond the parameter documentation — the baseline 3.

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 a Jev Score question that rates a state on an ordered scale the caller defines, and it names the shape of the result. This is clearly distinct from the jev_decide/jev_choice siblings by virtue of the ordered-scale semantics, though no sibling is named 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?

The phrase 'a single Score question' plus 'ordered scale you define' implies when this tool fits (grading/rubric-style evaluation), but the description never says when to prefer jev_decide or jev_choice instead, nor any prerequisites. Usage is inferable rather than stated.

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