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jev_ask

Read-only

Answer yes/no, multiple-choice, and scoring questions about files directly, without loading file contents into context. Batch questions per call to get compact, confidence-scored answers, saving tokens and improving speed.

Instructions

Answer typed questions about files via the Jev classifier without reading them into your context. noul=yes/no (criteria optional); choice: criteria {option:desc}; score: criteria [2-10 levels, low->high], answer is the 0-based level position (fractional allowed). Batch all questions per call. Out: {root?,results:{item:answer or {qid:answer}},unsure?}. noul -> P(yes) | [p,"?"]; choice/score -> [value,conf(,top2 probs)]. Open unsure items yourself. Weak at counting, math, dates, multi-step reasoning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathsNofiles, globs or dirs
textsNo{id:text}
verboseNo
questionsYes{id:{type:noul|choice|score,instructions,criteria}}
thresholdNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

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, but the description adds much more: output structure, per-type answer formats, confidence markers, unsure behavior, and known limitations. This is substantial behavioral context that annotations alone do not convey, with no contradiction.

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 dense and front-loaded, with the main purpose stated first and all critical format information compressed into a few lines. It contains little wasted text, though the compressed syntax is somewhat hard to parse quickly.

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

Completeness4/5

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

Despite having no output schema, the description covers input formats, output shape, per-type answer representation, batching, and known weaknesses, which is strong. But `threshold`, `verbose`, the meaning of `root?`, and the exact trigger for `unsure?` remain implicit, so it is not fully self-sufficient for a complex tool.

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 only 60%, so the description carries meaningful weight. It thoroughly defines the `questions` object's three types, criteria syntax, and 0-based level position, which are essential and not in the schema. However, `threshold` and `verbose` are not explained in the schema or the description, so parameter coverage is not complete.

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 first sentence states a specific verb ('Answer'), a resource ('typed questions about files'), and a distinguishing characteristic ('without reading them into your context'). It clearly enumerates the supported question types and output behavior, so an agent knows exactly what the tool does and does not need to disambiguate from siblings.

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

Usage Guidelines5/5

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

The description tells the agent when to use the tool ('without reading them into your context') and instructs it to batch all questions per call. It also gives explicit exclusions: 'Weak at counting, math, dates, multi-step reasoning' and directs the agent to 'Open unsure items yourself,' which is actionable guidance for deciding when and how to use it.

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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