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quartermaster

by app.surewhynot.quartermaster
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classify_text

[AI] Zero-shot classification of text into caller-supplied labels; the result is validated back into your label set so you can route on it. Costs $0.002.

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>",
      "labels": "<labels>"
    },
    "name": "classify_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
labelsYesComma-separated candidate labels (2-20)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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 that the tool uses AI, costs $0.002, and validates results back into the user's label set. This provides useful behavioral context beyond the structured fields.

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 sentences long with no wasted words. It efficiently communicates the tool's purpose, outcome, and cost.

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 no output schema and moderate complexity, the description covers key aspects: zero-shot classification, validation, routing use case, and cost. Could be more explicit about validation meaning, but overall meets needs.

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

Parameters2/5

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

Schema coverage is 50% (only 'labels' has a description). The tool description mentions 'caller-supplied labels' but does not add further detail about the 'text' parameter or clarify constraints like format or maximum length beyond what the schema already provides.

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 clearly states the action (zero-shot classification) and the resource (text into caller-supplied labels). It differentiates from siblings like moderate_text and embed_text by specifying the classification use case and validation into a label set for routing.

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

Usage Guidelines3/5

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

The description implies usage for routing decisions ('so you can route on it') and mentions cost as a consideration, but does not explicitly state when to use or not use this tool versus alternatives like moderate_text or embed_text.

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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