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Classify text

text_classify

Sort text into one of the labels you give, with a confidence. For routing, triage and moderation. Capped at 30,000 characters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to sort
labelsYesComma-separated labels to choose between

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removed
  3. Added

TDQS

A4/5.0
Behavior3/5

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

The description discloses some behavioral aspects: it returns a confidence and has a 30,000-character cap. Since there are no other annotations besides the title, the description carries the burden of transparency. It does not describe the return format in detail (e.g., how labels are returned, whether the label is a string or object) or error handling for over-length input, leaving some gaps.

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 exceptionally concise: two sentences that front-load the action, specify inputs, list use cases, and set a size limit. Every word adds value with no redundancy.

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 simple classification tool with no output schema, the description covers the key aspects: action, inputs, output (label with confidence), use cases, and a size limitation. It misses some details like output format and over-limit behavior, but these are relatively minor given the tool's simplicity.

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?

The schema fully describes both parameters (text and labels) with 100% coverage. The description adds minimal extra meaning, mainly reinforcing that labels are provided by the user. It does not enrich parameter understanding beyond the schema, so the baseline score of 3 is 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 clearly states the action (sort/classify), the resource (text), and the expected output (a label with confidence). It also names concrete use cases (routing, triage, moderation), which distinguishes it from sibling text tools like text_extract, text_summarise, and text_translate.

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?

The description provides explicit use cases (routing, triage, moderation), indicating when the tool is appropriate. It also mentions a character limit (30,000), which suggests a constraint. However, it does not explicitly mention when not to use it or name alternative 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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