Ludus FastMCP

"""Handler for automation and orchestration operations.""" from datetime import datetime, timedelta from typing import Any import hashlib from ludus_mcp.core.client import LudusAPIClient from ludus_mcp.utils.logging import get_logger logger = get_logger(__name__) class AutomationOrchestrationHandler: """Handler for automation and orchestration.""" def __init__(self, client: LudusAPIClient) -> None: """Initialize the automation orchestration handler.""" self.client = client async def create_deployment_pipeline( self, pipeline_name: str, stages: list[dict], user_id: str | None = None ) -> dict[str, Any]: """Define multi-stage deployment pipelines.""" try: pipeline_id = hashlib.md5(f"{pipeline_name}{datetime.now()}".encode()).hexdigest()[:8] # Validate stages for i, stage in enumerate(stages): if "name" not in stage: return {"status": "error", "error": f"Stage {i} missing 'name' field"} if "action" not in stage: return {"status": "error", "error": f"Stage {i} missing 'action' field"} pipeline = { "pipeline_id": pipeline_id, "name": pipeline_name, "created_at": datetime.now().isoformat(), "stages": stages, "total_stages": len(stages) } return { "status": "success", "timestamp": datetime.now().isoformat(), "pipeline": pipeline, "execution_guide": { "manual": "Execute each stage using corresponding ludus tools", "automated": "Use CI/CD platform (GitHub Actions, GitLab CI) to orchestrate", "example": "Stage 1: ludus.update_range_config -> Stage 2: ludus.deploy_range -> Stage 3: ludus.start_testing" } } except Exception as e: logger.error(f"Error creating deployment pipeline: {e}") return {"status": "error", "error": str(e)} async def schedule_range_tasks( self, task_type: str, schedule: str, parameters: dict | None = None, user_id: str | None = None ) -> dict[str, Any]: """Schedule recurring tasks (start/stop, snapshots).""" try: valid_tasks = ["power_on", "power_off", "snapshot", "health_check"] if task_type not in valid_tasks: return { "status": "error", "error": f"Invalid task_type. Must be one of: {valid_tasks}" } # Validate cron schedule schedule_parts = schedule.split() if len(schedule_parts) != 5: return { "status": "error", "error": "Invalid cron schedule format. Expected: 'minute hour day month weekday'" } task_id = hashlib.md5(f"{task_type}{schedule}{datetime.now()}".encode()).hexdigest()[:8] task_config = { "task_id": task_id, "task_type": task_type, "schedule": schedule, "parameters": parameters or {}, "created_at": datetime.now().isoformat(), "enabled": True, "next_run": self._estimate_next_run(schedule) } return { "status": "success", "timestamp": datetime.now().isoformat(), "task_configuration": task_config, "implementation": { "crontab": f"{schedule} ludus-fastmcp-task {task_type}", "systemd_timer": "Create systemd timer unit", "k8s_cronjob": "Deploy as Kubernetes CronJob" } } except Exception as e: logger.error(f"Error scheduling range task: {e}") return {"status": "error", "error": str(e)} async def auto_scaling( self, rules: list[dict], user_id: str | None = None ) -> dict[str, Any]: """Define auto-scaling rules based on resource usage.""" try: # Validate rules for i, rule in enumerate(rules): required_fields = ["metric", "threshold", "action"] for field in required_fields: if field not in rule: return { "status": "error", "error": f"Rule {i} missing required field: {field}" } scaling_config = { "enabled": True, "rules": rules, "created_at": datetime.now().isoformat(), "monitoring_interval_seconds": 60 } return { "status": "success", "timestamp": datetime.now().isoformat(), "scaling_configuration": scaling_config, "note": "Auto-scaling requires external monitoring and orchestration system", "implementation_guide": { "metrics_collection": "Use ludus.get_range_metrics periodically", "decision_engine": "Compare metrics against thresholds", "actions": "Execute ludus.update_range_config to adjust resources" }, "example_rule": { "metric": "cpu_utilization", "threshold": 80, "comparison": "greater_than", "action": "increase_cpus", "action_parameters": {"increment": 1} } } except Exception as e: logger.error(f"Error configuring auto-scaling: {e}") return {"status": "error", "error": str(e)} async def health_checks( self, check_interval_minutes: int = 5, alert_on_failure: bool = True, user_id: str | None = None ) -> dict[str, Any]: """Automated health checks with alerting.""" try: range_info = await self.client.get_range(user_id) health_check_config = { "enabled": True, "interval_minutes": check_interval_minutes, "alert_on_failure": alert_on_failure, "checks": [ { "name": "range_state", "description": "Check if range is in SUCCESS state", "endpoint": "ludus.get_range", "expected": "rangeState == SUCCESS" }, { "name": "vm_health", "description": "Check if all VMs are running", "endpoint": "ludus.get_range", "expected": "all VMs have status == running" }, { "name": "deployment_logs", "description": "Check for errors in logs", "endpoint": "ludus.get_range_logs", "expected": "no ERROR or FAILED messages" } ], "alert_channels": { "email": "Configure SMTP settings", "slack": "Use ludus.slack_notifications", "webhook": "Use ludus.webhook_integration" } } # Perform initial health check vms = range_info.get("VMs", []) running_vms = sum(1 for vm in vms if vm.get("status") == "running") health_status = "healthy" if running_vms == len(vms) else "degraded" return { "status": "success", "timestamp": datetime.now().isoformat(), "health_check_configuration": health_check_config, "current_health": { "status": health_status, "range_state": range_info.get("rangeState"), "vms_running": f"{running_vms}/{len(vms)}" }, "implementation": { "polling": "Use cron or systemd timer to run checks", "monitoring": "Integrate with Prometheus/Grafana for visualization" } } except Exception as e: logger.error(f"Error configuring health checks: {e}") return {"status": "error", "error": str(e)} def _estimate_next_run(self, schedule: str) -> str: """Estimate next run time (simplified).""" # In production, use croniter library return f"Estimated based on schedule: {schedule}"