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

Annotations already provide readOnlyHint=true, and the description adds useful behavioral detail: questions are 'answered independently in one request' and the tool returns probability distributions, which goes beyond what annotations state. There is no contradiction with 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?

Three sentences, with the core action front-loaded and the use case and batching note following naturally. Every sentence earns its place with no wasted words.

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 read-only prototyping tool with three well-documented parameters, the description covers the essential decision factors: what it runs, what it returns, and when to prefer it over jev_eval. The absence of an output schema is offset by the explicit mention of typed answers and probability 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 description coverage is 100%, so the schema fully documents model, state, and questions. The description adds the batching behavior of questions but no additional parameter syntax or format details, matching the baseline for well-covered schemas.

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 ('Run'), a resource ('a single state'), and an output ('typed answers with their probability distributions'), making the tool's purpose concrete. It also distinguishes itself from jev_eval by framing the use case as prototyping, so an agent can tell them apart 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?

The description explicitly says to use it 'to prototype question wording before committing it to code or to jev_eval,' which gives a clear when-to-use context and implies when not to use it (final evaluation). It also advises batching several questions per request. It doesn't explicitly mention jev_map, but the guidance is strong.

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 provide readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds no extra behavioral traits such as rate limits, auth needs, or side effects; the 'nearly free' remark hints at cost but is not a full disclosure. With annotations present, a 3 is appropriate because the description does not contradict and offers only marginal extra context.

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 sentences, each with a distinct purpose: the first enumerates the output metrics, the second gives usage timing, and the third explains the efficiency of passing multiple variants. There is no filler, and the core action is front-loaded.

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?

Given the tool has no output schema, the description appropriately enumerates the main output components (accuracy, Brier, ECE, AUC, threshold sweep, abstention curve, worst misses). It also covers the key workflow hint about evaluating before thresholding. However, it does not explain the distinction between inline items and dataset_path or the meaning of noul/choice/score types—though those are covered by the schema, so the description remains largely 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 description coverage is 100%, so the input schema already explains each parameter fully. The description adds a high-level tie to 'one or more wordings' (variants) and 'labeled items' (items/dataset_path) but does not provide any new parameter-specific semantics. Baseline 3 is correct.

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 action (score question variants against labeled items) and enumerates the outputs (accuracy, calibration, threshold sweep, abstention curve, worst misses), so the purpose is unambiguous. However, it does not explicitly distinguish this tool from siblings jev_map and jev_ask, so it earns 4 rather than 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 description gives clear usage timing: 'Run this before hard-coding any threshold, and rerun it after changing wording or model version.' It also notes that passing multiple variants is 'nearly free,' implying a best practice. It does not mention when NOT to use the tool or point to alternatives, which prevents a 5.

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; the description adds useful operational context beyond those: batch judging, per-item compact rows, per-question aggregates, and the ability to write full results to disk while only pulling back what matters. No contradiction with 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?

Two sentences, front-loaded with the core behavior, and each clause earns its place. It explains what the tool returns, why it exists, and what workflow it supports without any 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 an 11-parameter tool with nested question schemas and no output schema, the description plus fully documented schema covers the main invocation and selection needs. It does not spell out the exact row layout or default behaviors, but those are largely covered by the schema.

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 maps at a high level to filter/sort/save_path/include_rows concepts but does not add parameter-specific meaning beyond what the input schema already provides.

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 behavior: 'Judge every item in a list' and defines the return shape ('one compact row per item, plus per-question aggregates'). It clearly differentiates from single-item siblings like jev_eval and jev_ask by emphasizing multi-item mapping and scale.

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 clear context for when this tool is the right choice: 'triage and ranking at a scale where reading each item into context is the expensive part', with filter/sort/disk-write options to avoid pulling everything into context. It does not explicitly name when-not or alternative tools, so it stops short of a 5.

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