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AI Text Checker by carrierscan

Check if text is AI-written

check_ai_text
Read-only

Checks whether English or Ukrainian text (50-2,000 words) looks AI-written. Ukrainian text goes to our own Ukrainian detector (one detector). English text goes to the Desklib detector (DeBERTa-v3 classifier, gives an AI likelihood for the whole text and for ~500-word sections). Detectors make mistakes; never present the result as proof. Text is not stored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesEnglish or Ukrainian text to check, 50-2,000 words

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Goes well beyond readOnlyHint/openWorldHint by disclosing model routing (Ukrainian custom detector vs. Desklib DeBERTa-v3), the sectioned AI-likelihood output for English, the fallibility caveat ('never present the result as proof'), and that text is not stored. That is exactly the extra context annotations can't convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences front-load scope, then routing, then caveats. Nothing is filler, though the detector-technology detail is somewhat verbose for an agent's decision-making needs.

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 one-parameter, no-output-schema read tool with annotations covering safety, the description is nearly complete: scope, routing, output shape for English, caveats. It could mention what Ukrainian output looks like (it says 'one detector' but not the result format), a minor gap.

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% and the single parameter (text) is fully described in-schema with the same 50-2,000 words and language constraints. The description adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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 (checks) plus resource (text) and even the conclusion being produced (whether it looks AI-written). The language and length scope is stated up front, so an agent knows exactly what this tool does and its limits without opening the schema.

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?

Clearly implies when to use it (English or Ukrainian text within 50-2,000 words) and names the routing per language. No sibling tools exist, so alternatives needn't be named, but it stops short of explicitly saying what to do for other languages or lengths (reject vs. error).

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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