openjev-mcp

Jev noul

jev_noul

Read-onlyIdempotent

Ask one yes/no question about your data and get a probability between 0 and 1, where near 1 means yes, near 0 means no, and near 0.5 signals uncertainty.

Instructions

Ask one yes/no question and get its probability.

Use when the condition either holds or does not, and the probability is more useful than a category. Name the condition precisely in instructions; optionally describe the two outcomes in criteria.true and criteria.false, or omit criteria when the instructions already define both.

Returns a value between 0 and 1: near 1 yes, near 0 no, near 0.5 uncertain. It measures probability, not intensity, and there is no separate confidence field — a confident no is near 0, not near 0.5, so read low values as evidence against rather than as low confidence.

Input Schema

Name	Required	Description
`model`	No	Model alias. Omit to use the server default (`openjev`).
`state`	Yes	The content the questions are judged against: text, an object of named fields, or an array of records. The state is judged as one whole, so an array of tickets gets one blended answer rather than one per ticket; to judge records one by one, use `jev_batch`. Reference a nested field from `instructions` with a dotted path in backticks, e.g. `account.plan`. Fetch external records first — a URL here is not a request to browse — and note that this API accepts no image, audio, or file uploads.
`criteria`	No	Optional descriptions of the two outcomes. Omit when `instructions` already defines both.
`question_id`	No	Label for this question in the response. Defaults to the primitive name.
`instructions`	Yes	The judgment to make, written out in full. A clear specific string is usually enough; use an object or array when the judgment, its scope, and its constraints belong together. The question id is not sent to the model, so never rely on it to carry meaning.

Output Schema

Name	Required	Description
`hints`	No	Non-blocking advice about this request, when there is any.
`model`	No
`usage`	No
`answer`	Yes
`question_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.2.0Oct 14, 2026

changedInput schema / properties / state / description

Previous value: -"The content the questions are judged against: text, an object of named fields, or an array of records. Reference a nested field from `instructions` with a dotted path in backticks, e.g. `account.plan`. Fetch external records first — a URL here is not a request to browse — and note that this API accepts no image, audio, or file uploads."New value: +"The content the questions are judged against: text, an object of named fields, or an array of records. The state is judged as one whole, so an array of tickets gets one blended answer rather than one per ticket; to judge records one by one, use `jev_batch`. Reference a nested field from `instructions` with a dotted path in backticks, e.g. `account.plan`. Fetch external records first — a URL here is not a request to browse — and note that this API accepts no image, audio, or file uploads."

First observedv0.1.1Sep 22, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint and non-destructiveness, but the description adds substantive behavior the annotations cannot: the 0-1 output scale and how to read it, the warning that there is no separate confidence field (a confident no is near 0), the fact that an array of records is blended into one answer, and that URLs are not browsed and no media uploads are accepted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action in one sentence, then usage, then return interpretation. Three paragraphs with little waste, though the closing paragraph on reading low values is slightly discursive for the space it occupies.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return structure need not be described, yet the description still supplies the interpretive meaning of the returned number. Combined with the state-handling and input-modality caveats, nothing an agent needs to invoke this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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, but the description adds real meaning beyond the schema: it explains when criteria.true/false may be omitted because instructions already define both outcomes, and stresses naming the condition precisely in instructions rather than relying on question_id.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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 one yes/no question and get its probability') and the binary framing inherently separates it from jev_choice and jev_score, while the state description explicitly routes per-record judgments to jev_batch. An agent can identify this as the binary-probability primitive without opening a sibling schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a concrete selection condition ('use when the condition either holds or does not, and the probability is more useful than a category') and names jev_batch as the alternative for per-record judging. It stops short of an explicit when-not statement or a direct contrast with jev_choice/jev_score.

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

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