openjev-mcp

Jev score

jev_score

Read-only

Rate any text or data against a custom ordered scale you define, returning a numeric score, level legend, probability distribution, and confidence.

Instructions

Ask one question whose answer is a position on an ordered scale you define.

Use for degree, severity, or intensity. criteria lists the levels from lowest to highest; a three-level scale returns a score from 0 to 2, and values between integers are positions between levels — the score is the probability-weighted mean of level numbers, not a percentage or a category id. Give every level a self-contained description ("no deadline expressed", "wants a response soon", "explicit deadline"), never a relative one like "more urgent than the level below".

Returns the score, the legend mapping level numbers to their descriptions, the full distribution, and confidence. Two different distributions can produce the same score, so read probabilities when the difference matters — all weight on level 1 and an even split between 0 and 2 both average to 1.

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`	Yes	The ordered levels, lowest first. Each level is a self-contained description (a string, or an object/array holding meaning and examples). A three-level scale returns a score from 0 to 2; fractional scores fall between levels, so each level must stand on its own rather than say "same as the level below".
`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

A4.4/5.0

Behavior5/5

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

Annotations already mark it read-only and open-world; the description adds substantial operational detail: scores are probability-weighted means of level numbers, not percentages or category IDs, fractional values fall between levels, and identical scores can arise from different distributions, so `probabilities` must be read when that distinction matters. It also discloses the output fields.

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?

Every sentence carries distinct value: purpose, usage scope, criteria construction rules, output contract, and an interpretation caveat. It is front-loaded with the core action and remains efficient despite its length.

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 100% schema coverage, a rich output schema, and read-only annotations, the description is fully sufficient. It covers how to phrase criteria, how scoring works, what the response contains, and a pitfall about equal scores from different distributions.

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 coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining that `criteria` levels are ordinal and scored 0..n-1 with fractional positions, and that the score is a probability-weighted mean rather than a category identifier, which is essential for correct usage.

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 first sentence states a precise action: 'Ask one question whose answer is a position on an ordered scale you define.' This clearly identifies the tool as an ordinal/scalar judgment maker and differentiates it from generic ask tools by concept, though it does not explicitly name or contrast the sibling tools.

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 gives an explicit usage context: 'Use for degree, severity, or intensity.' It also warns against relative criteria and explains when to inspect probabilities. However, it does not state when not to use this tool or name alternatives among siblings.

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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