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Grokzilla micro pay

ai_detector

Estimate whether text (or a web page's text) is AI-written using a trained classifier: about 78% accurate on a held-out benchmark (not a guarantee). Uncertain results suggest a rerun on a longer section (300+ words); tell phrases are only a secondary 'possible AI influence' signal. Price $0.01 via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
textNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the burden well: it discloses the ~78% benchmark accuracy and explicitly warns it is 'not a guarantee', flags uncertainty behavior, and states the $0.01 x402 payment requirement. It omits whether fetching a url is side-effect-free, how errors surface, or what the response shape is.

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?

Dense but front-loaded: purpose first, then accuracy caveat, then rerun guidance, then pricing. Every clause carries information, though the run-on middle sentence packing rerun advice and tell-phrase caveats together is slightly heavy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description should describe the return value — it never says whether the tool returns a label, probability, or phrase list, only alluding to 'tell phrases'. Cost, accuracy and input handling are covered, but the output contract is a meaningful gap for a classifier tool.

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 0% and both parameters are undocumented in the schema, so the description must compensate. It does clarify the url-vs-text duality ('text (or a web page's text)'), but never states that the two are mutually exclusive or that one is required despite both being optional in the schema.

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 ('estimate whether') and resource ('text is AI-written using a trained classifier'), plus the alternative input form (web page text). No sibling tool in the list does anything comparable, so the purpose is unambiguous 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?

Gives concrete operational guidance: rerun on a longer section (300+ words) when results are uncertain, and treat tell-phrases as only a secondary signal. It does not need to name an alternative tool since none exists, but it also never states the precondition that at least one of url/text must be supplied.

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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