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Spam Intent Classifier MCP
x402
Spam Intent Classifier MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.1/call, status unknown (last checked 2026-10-16).
Classifies whether a given text input carries spam intent, returning a classification result with confidence signals.
Price / call$0.1
Success—
StatusUnknown
How this service works
The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.
Output
Returns a classification indicating whether the submitted text is spam or not spam, typically including a label and a confidence score reflecting how strongly the model believes the input carries spam intent.
Example use cases
Email inbox spam screening
“Before I process this incoming email, can you check if it's spam? Here's the body: 'Dear user, your account has been compromised. Send your password immediately to restore access.'”
User comment moderation pipeline
“A new comment just came in on our platform — can you tell me if it looks like spam? The text is: 'Buy cheap Rolex watches!! Best prices guaranteed! Visit our site now!!!'”
Lead form submission filtering
“Someone submitted a contact form with this message — run it through your spam intent classifier and tell me if it's legit: 'Make $5000 a week from home with no experience needed. Limited spots available, act now!'”
Technical details
Endpoint
POSThttps://api.m2mcent.com/spam-intent-classifier-mcp/api/process?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a dedicated, pay-per-call spam intent classifier without managing your own ML infrastructure. Prefer it over general-purpose LLM prompting for spam detection when you want a consistent, specialized signal, especially in automated pipelines processing user-generated content, form submissions, or incoming messages at scale.
Side effects
- Empty or missing 'data' field returns a validation error
- Extremely short or ambiguous inputs may yield low-confidence classifications
- Non-English text may reduce classification accuracy if the model is English-optimized
- Very long inputs may be truncated or rejected
- Rate limiting or payment failure (x402) if USDC balance is insufficient
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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