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Context Targeting Oracle MCP
x402
Context Targeting Oracle MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.15/call, status unknown (last checked 2026-10-13).
Analyzes input data to determine contextual targeting signals, categories, or recommendations for ad/content targeting decisions.
Price / call$0.15
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 contextual targeting signals, category classifications, and audience segmentation data derived from the submitted text or data payload, enabling downstream ad serving or content routing decisions.
Example use cases
Classify article for programmatic ad targeting
“I have this blog post about sustainable travel — can you run it through the context targeting oracle and tell me what IAB content categories and targeting signals it fits so I know which ads to serve?”
Pre-flight content suitability check
“Before we publish this product description on our ad exchange, can you check what context targeting signals it triggers so we can make sure it's brand-safe and hitting the right audience?”
Audience segmentation for campaign planning
“Here's a batch of landing page copy for our new fitness app — what contextual targeting categories does this content fall into so we can align our media buy accordingly?”
Technical details
Endpoint
POSThttps://api.m2mcent.com/context-targeting-oracle-mcp/api/process?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need oracle-grade contextual analysis of text or content for targeting purposes — particularly in programmatic advertising, content recommendation, or brand-safety pipelines where you need IAB-style category signals or audience segment classifications from raw content.
Side effects
- Empty or missing 'data' field returns validation error
- Overly long or malformed text may result in processing timeout
- Ambiguous content may return low-confidence or null targeting categories
- Non-text binary data submitted as string may produce unexpected classification results
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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