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jev-mcp

by jkudish
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JavaScript
Remote

Calibrated probability for propositions

jev_noul

Return calibrated probabilities for multiple stated propositions in one batched request, using optional context to inform each judgment while flagging likely, unlikely, or uncertain outcomes.

Instructions

Return a calibrated probability for each stated proposition with TypeSafe Jev, in one batched request: high means likely, low means unlikely, middling means genuinely uncertain. Supplied context informs the judgment but is not a proof guarantee; to test claims strictly against evidence, including whether the evidence is merely silent, use jev_verify instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoOptional context the propositions are judged against: one document or evidence items. When omitted, the model's own knowledge applies.
auto_acceptNoDecisiveness threshold: probability at or above this marks the proposition likely, at or below (1 - this) unlikely, between them uncertain. Must exceed 0.5. Default 0.85.
propositionsYesPropositions to judge, each a single testable statement. Up to 64 per call, 2000 chars each.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.1
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Addedv0.9.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and succeeds substantially: it explains how to interpret the probability output (high=likely, low=unlikely, middling=uncertain), states that supplied context informs judgment but is not a proof guarantee, and notes the batched nature. It does not cover operational details such as permissions, failure behavior, or determinism.

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 two tightly written sentences: the first front-loads the core purpose and output interpretation, and the second routes to the alternative. Every phrase earns its place, with no redundant restatement of the schema.

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?

There is no output schema and no annotations, so the description must explain both output meaning and behavior. It does explain that a calibrated probability is returned per proposition and how to interpret it, plus the context limitation, but it omits some operational details that would make it fully self-contained.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters, including context, propositions, and auto_accept. The description adds some framing around context not being proof, but it does not provide syntax or meaning beyond the schema, making the baseline 3 appropriate.

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 specific verb and resource: return a calibrated probability for each stated proposition in a batched request. It also distinguishes the tool from its sibling by naming jev_verify as the alternative for strict evidence testing.

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?

It explicitly gives the condition for using this tool versus jev_verify: use jev_verify when testing claims strictly against evidence, including whether evidence is silent. This is a clear when-to-use and when-to-use-alternative rule.

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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