openjev-mcp

Jev noul

jev_noul

Read-only

Ask a yes/no question about your data and get a probability between 0 and 1, where near 1 means yes and near 0 means no. Define the condition in plain language to judge text, objects, or records.

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

TableJSON 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. 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

TableJSON 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.

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?

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters5/5

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.

Purpose4/5

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.

Usage Guidelines4/5

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.

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