# Anomaly Detection Sentinel MCP

> Anomaly Detection Sentinel MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.1/call, status unknown (last checked 2026-10-12).

Analyzes input data to detect anomalies, outliers, or unexpected patterns using a sentinel-style monitoring approach

## Facts

- Endpoint: POST https://api.m2mcent.com/anomaly-detection-sentinel-mcp/api/process?utm_source=zero.xyz
- Price: $0.1/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-12
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/anomaly-detection-sentinel-mcp-7a151251
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_0EXYSR8uhSS3ZIslEJXqK

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability anomaly-detection-sentinel-mcp-7a151251 -d '<json body>'
```

Example prompt: Can you scan this dataset for anomalies and flag anything that looks unusual or out of the ordinary? Here's the data: [1.2, 1.3, 1.1, 45.7, 1.2, 1.4, 0.9]

## When to prefer this

Choose this endpoint when you need a fast, pay-per-call anomaly detection service for string-encoded data, particularly in agentic workflows where you want to programmatically flag outliers without setting up dedicated ML infrastructure. Best suited for one-off or low-frequency anomaly checks rather than continuous high-volume streaming.

## Known failure modes

- Empty or malformed data string returns an error response
- Non-numeric or unparseable data may fail detection
- Extremely small datasets may yield unreliable anomaly scores
- Service unavailability may return HTTP 402 or 5xx errors
- Payment failure via x402 protocol results in request rejection

## 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 structured result indicating whether anomalies were detected in the submitted data, along with flagged data points, anomaly scores or indicators, and contextual details about the nature of the detected irregularities.

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "success": true
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/anomaly-detection-sentinel-mcp-7a151251/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.m2mcent.com](https://www.zero.xyz/host/api.m2mcent.com/llms.txt)