IBM i MCP Server

""" Performance Investigation Workflow This complex workflow coordinates multiple agents to investigate and diagnose performance issues on an IBM i system using Agno workflow conventions. Use case: Users report slowness, admin needs to identify root cause and provide recommendations. Workflow steps: 1. Performance agent gathers comprehensive metrics 2. Discovery agent identifies relevant monitoring services 3. Performance agent performs deep analysis using reasoning 4. Generates detailed report with prioritized recommendations An IBM i admin runs this workflow when: - Users report system slowness - Scheduled deep performance analysis is needed - After system changes to verify no performance degradation """ from textwrap import dedent from agno.workflow import Workflow, Step from agno.workflow.types import StepInput, StepOutput from agno.db.postgres import PostgresDb from agents.ibmi_agents import ( get_performance_agent, get_sysadmin_discovery_agent, ) from db.session import db_url # Create agents with reasoning enabled performance_agent = get_performance_agent(model="openai:gpt-4o", enable_reasoning=True) discovery_agent = get_sysadmin_discovery_agent(model="openai:gpt-4o", enable_reasoning=True) # Step 1: Gather initial performance metrics initial_metrics_step = Step( name="InitialMetrics", agent=performance_agent, description="Gather comprehensive performance metrics for investigation", ) # Step 2: Identify relevant monitoring services monitoring_services_step = Step( name="MonitoringServices", agent=discovery_agent, description="Identify additional monitoring services that might help with diagnosis", ) # Step 3: Custom function for deep analysis that accesses previous steps def deep_performance_analysis(step_input: StepInput) -> StepOutput: """ Custom function that performs deep analysis using data from previous steps. """ # Access specific step outputs metrics_data = step_input.get_step_content("InitialMetrics") or "" services_data = step_input.get_step_content("MonitoringServices") or "" # Create comprehensive analysis prompt analysis_prompt = f""" Perform deep performance analysis using all available data: ## INITIAL METRICS GATHERED: {metrics_data[:1000]}... [truncated for analysis focus] ## AVAILABLE MONITORING SERVICES: {services_data[:500]}... [truncated for analysis focus] ## YOUR DEEP ANALYSIS TASKS: 1. **Pattern Analysis**: - Analyze CPU usage patterns vs. job activity - Examine memory pool utilization vs. thread counts - Identify I/O patterns and potential bottlenecks - Look for temporal trends in the metrics 2. **Use Reasoning Tools**: - Use think() to structure your diagnostic approach - Use analyze() to examine metric relationships and correlations - Consider multiple hypotheses for root causes - Evaluate system architecture implications 3. **Identify Specific Issues**: - Resource contentions - Configuration problems - Capacity limitations - Workload imbalances 4. **Assess Severity and Impact**: - Critical issues requiring immediate action - Performance degradation affecting users - Efficiency improvements for optimization - Preventive measures for future issues Show your reasoning process for the diagnosis. """ return StepOutput(step_name="DeepAnalysis", content=analysis_prompt, success=True) deep_analysis_step = Step( name="DeepAnalysis", executor=deep_performance_analysis, description="Perform deep analysis using reasoning tools and all collected data", ) # Step 4: Run the deep analysis with the performance agent analysis_execution_step = Step( name="AnalysisExecution", agent=performance_agent, description="Execute the deep analysis plan with reasoning" ) # Step 5: Generate prioritized recommendations recommendations_step = Step( name="Recommendations", agent=performance_agent, description="Generate prioritized recommendations based on complete analysis", ) # Create the complete workflow performance_investigation_workflow = Workflow( name="IBM i Performance Investigation", description="Multi-step performance investigation with reasoning and prioritized recommendations", steps=[ initial_metrics_step, monitoring_services_step, deep_analysis_step, # Custom function that prepares analysis prompt analysis_execution_step, # Agent executes the analysis recommendations_step, ], db=PostgresDb(id="agno-storage", db_url=db_url), ) if __name__ == "__main__": # Run the complete investigation workflow performance_investigation_workflow.print_response( message=dedent(""" We need to investigate system performance issues. CONTEXT: - Users are reporting system slowness - This is a comprehensive performance investigation - We need to identify root causes and provide actionable recommendations INITIAL METRICS TASK: Gather comprehensive performance metrics including: - Overall system status (CPU, memory, I/O) - System activity metrics - Memory pool utilization - HTTP server performance - Active job information for top CPU consumers - Temporary storage usage - System values affecting performance MONITORING SERVICES TASK: After metrics are gathered, identify: - Service categories related to performance monitoring - QSYS2 services for deeper performance analysis - Services that could help with root cause analysis - Job queue and process management services RECOMMENDATIONS TASK: Based on the complete analysis, provide: 1. **Immediate Actions** (Critical - Do Now): - Issues causing active performance problems - Steps to stabilize the system - Estimated time and expected impact 2. **Short-term Improvements** (High Priority - This Week): - Configuration optimizations - Resource reallocation recommendations - Implementation guidance 3. **Long-term Optimizations** (Medium Priority - This Month): - Capacity planning recommendations - Architecture improvements - Best practice implementations 4. **Preventive Measures**: - Early warning indicators to monitor - Alert thresholds to configure - Regular maintenance tasks For each recommendation explain WHY, WHAT, IMPACT, and RISKS. """), markdown=True, stream=True, stream_intermediate_steps=True, )