detect_log_anomalies
Identify unusual patterns in log data by analyzing error frequency, repetition, and timestamps to detect potential issues in systems.
Instructions
Detect anomalies in log data using error frequency, pattern repetition, and timestamp analysis.
Args:
logs: Raw log content (newline-separated entries).
baseline_patterns: Optional expected error patterns for comparison.
severity_threshold: "low" (most sensitive), "medium", or "high" (least sensitive).
Returns:
Dict[str, Any]: Keys: anomaly_detected (bool), anomaly_details, analysis_summary.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| logs | Yes | ||
| baseline_patterns | No | ||
| severity_threshold | No | medium |