umpirix

Yes or no, with a probability

noul

Answer a yes/no question about any text and return the probability it is yes plus a 0.5-threshold decision for gates and flags like spam, refund requests, or human review.

Instructions

Answer a yes/no question about text. Returns the probability that the answer is yes (0 to 1) and the yes/no answer at 0.5. Use it for gates and flags: is this spam, is this a refund request, does this need a human.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>",
      "question": "<question>"
    },
    "name": "noul"
  }
}

Input Schema

Name	Required	Description
`text`	Yes	The text to judge: a message, ticket, review, document or any other content.
`model`	No	Optional model id (for example system-one or auto). Defaults to the server's default model.
`question`	Yes	A yes/no question or a statement to verify.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 11, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the return shape (probability 0 to 1) and the decision rule (yes/no threshold at 0.5), which an agent needs to interpret output correctly. It omits latency, cost, and determinism/consistency characteristics, but for a stateless inference call those are secondary.

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?

Three short sentences, front-loaded with the core action, then the return contract, then usage. Every sentence contributes and nothing is padded.

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 simple three-parameter classification tool with no output schema and fully described parameters, the definition covers action, return values, decision threshold, and typical use cases. Nothing an agent needs to invoke it correctly is missing.

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 coverage is 100%, so all three parameters are already documented in the schema, and the description only restates that the judgement is 'about text'. It adds no syntax, length, or model-selection guidance beyond what the schema provides, so the baseline 3 applies.

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?

States a specific verb and resource: answer a yes/no question about supplied text, returning a yes-probability. It's clear what the tool does and its binary-plus-confidence output shape, though it never explicitly contrasts itself with the siblings (choice, decide, score), so an agent must infer the difference from the word 'yes/no' alone.

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?

Gives a clear usage frame ('Use it for gates and flags') plus three concrete example questions (spam, refund request, human escalation) that map directly onto real invocation contexts. No explicit when-not-to-use or named alternatives are offered, which keeps it short of a 5.

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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