umpirix

Rate on a scale

score

Rates text on an ordered scale, returning the expected rating, closest level, confidence, and probability for each level to assess urgency, severity, sentiment, or quality.

Instructions

Rate text on an ordered scale. Returns the expected score (0 is the first level), the closest level, a confidence, and a probability for every level. Use it for urgency, severity, sentiment or quality.

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>",
      "levels": "<levels>"
    },
    "name": "score"
  }
}

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.
`levels`	Yes	Level labels from lowest to highest, e.g. ['low', 'medium', 'high'].
`question`	Yes	What to rate, e.g. 'How urgent is this?'

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 11, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return format (expected score, closest level, confidence, probabilities) but omits other behavioral traits such as read-only nature, determinism, or rate limits.

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?

Two sentences with zero waste; purpose and return format are front-loaded, and usage examples follow efficiently.

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?

For a tool with 4 parameters, no output schema, and no annotations, the description covers purpose, usage, and return values well. It leaves minor gaps around model behavior and safety profile, but is largely complete.

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. The description adds minor semantic value by clarifying the zero-indexing of levels, but does not add meaning beyond what the schema provides.

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?

The description states a specific verb (rate) and resource (text on an ordered scale), and explains what the tool returns. It does not differentiate itself from sibling tools like choice or decide, which limits it to a 4 rather than a 5.

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?

It provides clear usage contexts: 'urgency, severity, sentiment or quality.' No explicit when-not guidance or alternatives are given, but the use cases are well-defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server