$ man ai-content-detector
/ai-content-detector(1)
PRICE / CALL
$0.03
USDC · base mainnet · scheme: exact
METHOD
POST
CLUSTER
prooflayerCATEGORY
ai
STATUS
● live
NAME
ai-content-detector — ai content detector / gpt detector / chatgpt plagiarism checker
SYNOPSIS
POST https://x402.org/v1/ai-content-detector
Content-Type: application/json
X-PAYMENT: <signed-transferWithAuthorization>
{ ... }↳ first call →
402 Payment Required. Sign USDCtransferWithAuthorization, retry with theX-PAYMENT header.DESCRIPTION
AI content detector / GPT detector / ChatGPT plagiarism checker. Calibrated probability (0-1), verdict, suspicious phrases, per-axis style signals (em-dash overuse, hedge phrases, formulaic transitions).
INPUT — request schema
| property | type | description | req? |
|---|---|---|---|
| text | string | 100-20,000 chars. | required |
OUTPUT — response shape
| field | type | description |
|---|---|---|
| probability_ai_generated | number | Calibrated 0-1 probability that the submitted text was AI-generated. |
| verdict | string | Short label summarizing the call, like "likely_ai", "likely_human", or "uncertain". |
| confidence | number | 0-1 score for how strongly the per-axis signals agree on the verdict. |
| reasoning | array | Ordered list of human-readable reasons backing the verdict, citing which signals fired. |
| suspicious_phrases | array | Verbatim snippets from the input flagged as characteristic AI phrasing. |
| style_signals | object | Per-axis style scores: em-dash overuse, hedge phrases, formulaic transitions, and similar. |
| input_chars | number | Character count of the submitted text after normalization. |
| model | string | Identifier of the classifier model that produced the verdict. |
EXAMPLES — two ways to call
EXAMPLE 1 · curl
curl -X POST https://x402.org/v1/ai-content-detector \
-H 'Content-Type: application/json' \
-d '{ }'first response =
402 Payment Required with payment requirements; sign + retry with X-PAYMENT.EXAMPLE 2 · mcp
# install once claude mcp add x402 --command "npx x402-deployer-mcp" # then ask Claude Code: # "use the ai-content-detector tool to ..."
MCP server handles payment automatically — your coding agent just calls the tool by name.
METADATA
- tags
- ai-detectioncontentmoderationllmclassify
- env
- VENICE_API_KEY
- methods
- POST
- cluster
- prooflayer
- price
- $0.03 USDC per call
ADJACENT — other endpoints in prooflayer
| endpoint | description | price |
|---|---|---|
| dep-risk-summary | repo dependency risk audit / package.json + lockfile vetter / unpinned dep detector / transitive dep counter / requirements.txt audit / p… | $0.03 |
| github-repo-health | GitHub repo health score / open-source maintainability checker. | $0.03 |
| package-risk-npm | npm package risk score / supply-chain scanner / typosquat detector. | $0.03 |
| prompt-injection-surface | AI prompt injection surface scanner / LLM call-site audit / unsanitized user input in prompts detector / system-message mixing flag / unb… | $0.03 |
| db-migration-risk | DB migration risk audit / SQL migration safety check / DROP COLUMN detector / unsafe ALTER TABLE detector / Postgres CREATE INDEX CONCURR… | $0.02 |
| deploy-config-risk | deploy config audit / Dockerfile lint / vercel.json hardening / wrangler.toml review / docker-compose.yml safety / fly.toml secrets check… | $0.02 |
| secrets-exposure-check | secrets exposure scan / hardcoded API key detector / .env-committed-key audit / Next.js client env leak detector / pre-deploy secret gate. | $0.02 |
| pypi-package-risk | PyPI package risk score / Python supply-chain scanner. | $0.01 |
SEE ALSO