askjev

Ask Jev

ask

Read-only

Get calibrated probabilistic answers to yes/no, scale, or choice questions about provided material. Use Jev's non-generative judgment model for quick second opinions, classification, ratings, or checks.

Instructions

Ask Jev, a fast non-generative judgment model, one or more questions about some material you already have. Use it for a quick second opinion, a classification, a rating, or a yes/no check instead of reasoning it out yourself or spawning a sub-agent.

Write each question in plain language. Jev decides whether it is a yes/no question, a scale, or a choice:

Yes/no: "Does this ticket ask for a refund?" You get the probability of yes.
Choice: pass "options" with the named alternatives. "Which team owns this?" with options ["billing", "platform"].
Scale: pass "options" as ordered levels, lowest first. "How urgent is this?" with options ["can wait", "this week", "today"]. Without options, a built-in rubric is picked for you:
- intensity: not at all < slightly < moderately < very < extremely
- quality: poor < below average < acceptable < good < excellent
- severity: trivial < minor < moderate < major < critical
- likelihood: very unlikely < unlikely < uncertain < likely < very likely
- sentiment: very negative < negative < neutral < positive < very positive
- frequency: never < rarely < sometimes < often < always
- agreement: strongly disagree < disagree < neutral < agree < strongly agree

Every answer carries probabilities and Jev's confidence, plus "routing" showing how the question was interpreted. Read the confidence: a low value means the material does not settle the question, so add context or decide another way.

Requires TYPESAFE_API_KEY in the server's environment.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`state`	Yes	The material to judge: text, a JSON object, or a JSON array.
`questions`	Yes	Questions about the same state, answered in one round trip.

Output Schema

TableJSON Schema

Name	Required	Description
`model`	Yes	The Jev model that answered.
`usage`	Yes	Token usage summed over every Jev call made.
`answers`	Yes	One entry per question, in input order. A question that could not be answered has kind "error"; the others are still answered.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 17, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint; the description adds substantial behavioral context: it is non-generative, every answer carries probabilities and confidence, low confidence signals inconclusive material, and routing explains interpretation. It also discloses the TYPESAFE_API_KEY requirement, going well beyond 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.

Conciseness4/5

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

The description is long but well-structured with clear sections and bullet points. It is front-loaded with the purpose, then dives into usage examples. Every sentence adds value—nothing is filler. It could be slightly more compact, but the detail is justified by the tool's flexibility.

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?

Given the tool's complexity (two parameters, many question types, built-in rubrics) and that an output schema exists (so return format is covered elsewhere), the description is remarkably complete. It explains question interpretation, option semantics, confidence handling, and the API key requirement—nothing an agent needs to call it correctly is missing.

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 enriches both parameters. It explains how 'state' can be text/JSON, how 'questions' should be phrased, the role of 'options' for choices and scales, and provides built-in rubrics for common scales. This adds meaning far beyond the schema's basic descriptions.

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 description states a clear verb ('Ask') and resource ('Jev', a fast non-generative judgment model), and defines the exact purpose: answering one or more questions about existing material. It distinguishes itself from reasoning manually or spawning a sub-agent, which is especially useful given no siblings are listed.

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 explicitly says when to use it ('quick second opinion, classification, rating, or yes/no check') and gives a comparison ('instead of reasoning it out yourself or spawning a sub-agent'). It could add explicit 'when not to use' (e.g., for generative tasks), but the guidance is clear and actionable.

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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