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umpirix

umpirix

by caelum0x
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Choose one option

choice

Classify any text into exactly one of your provided options and get back the chosen label, a confidence score and per-option probabilities. Use it for routing, labelling and triage without parsing model replies.

Instructions

Classify text into exactly one of the given options. Returns the chosen option, a confidence, and a probability for every option. Use it for routing, labelling and triage instead of asking a model and parsing its reply.

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>",
      "options": "<options>"
    },
    "name": "choice"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text to judge: a message, ticket, review, document or any other content.
modelNoOptional model id (for example system-one or auto). Defaults to the server's default model.
optionsYesThe options: an array of names, or an object mapping each name to a short description of when it applies. At least two.
questionYesWhat to decide, e.g. 'Which team should handle this?'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations supplied, the description carries the burden and does well: it discloses that exactly one option is chosen and that the response includes the chosen option, a confidence, and a probability per option. It does not mention model invocation costs, latency, or behavior when the text is ambiguous or options overlap.

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 tight sentences: purpose, return shape, usage guidance, front-loaded with no wasted wording.

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?

Covers purpose, output contents, and usage rationale for a four-parameter classification tool, which is enough to call it correctly. Minor gaps remain on how option descriptions are used and on failure or ambiguity behavior, but nothing essential is missing.

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 schema already documents all four parameters, and the description adds meaning by stating the single-choice constraint and the return contract tied to the options parameter. It does not elaborate on how option descriptions influence classification or how `model` affects results.

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?

States a specific verb and resource ('Classify text into exactly one of the given options') and names the exact output shape, which distinguishes it from siblings like decide or score.

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 concrete use cases (routing, labelling, triage) and explicitly says to prefer it over asking a model and parsing its reply, so the when-to-use context is clear. It does not state when not to use it or how it relates to the sibling tools.

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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