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CFM Tips - Cost Optimization MCP Server

by aws-samples
example_output_with_docs.py5.45 kB
#!/usr/bin/env python3 """ Example showing how documentation links appear in tool outputs """ import json from utils.documentation_links import add_documentation_links def show_example_outputs(): """Show examples of how documentation links appear in different tool outputs""" print("CFM Tips - Documentation Links Feature Examples") print("=" * 60) print() # Example 1: EC2 Right-sizing Analysis print("1. EC2 Right-sizing Analysis Output:") print("-" * 40) ec2_result = { "status": "success", "data": { "underutilized_instances": [ { "instance_id": "i-1234567890abcdef0", "instance_type": "m5.large", "finding": "Overprovisioned", "recommendation": { "recommended_instance_type": "m5.medium", "estimated_monthly_savings": 45.50 } } ], "count": 1, "total_monthly_savings": 45.50 }, "message": "Found 1 underutilized EC2 instances via Compute Optimizer" } enhanced_ec2 = add_documentation_links(ec2_result, "ec2") print(json.dumps(enhanced_ec2, indent=2)) print("\n" + "=" * 60 + "\n") # Example 2: S3 Optimization Analysis print("2. S3 Optimization Analysis Output:") print("-" * 40) s3_result = { "status": "success", "comprehensive_s3_optimization": { "overview": { "total_potential_savings": "$1,250.75", "analyses_completed": "6/6", "failed_analyses": 0, "execution_time": "45.2s" }, "key_findings": [ "Found 15 buckets with suboptimal storage classes", "Identified $800 in potential lifecycle savings", "Discovered 25 incomplete multipart uploads" ], "top_recommendations": [ { "type": "storage_class_optimization", "bucket": "my-data-bucket", "potential_savings": "$450.25/month", "action": "Transition to IA after 30 days" } ] } } enhanced_s3 = add_documentation_links(s3_result, "s3") print(json.dumps(enhanced_s3, indent=2)) print("\n" + "=" * 60 + "\n") # Example 3: RDS Optimization Analysis print("3. RDS Optimization Analysis Output:") print("-" * 40) rds_result = { "status": "success", "data": { "underutilized_instances": [ { "db_instance_identifier": "prod-database-1", "db_instance_class": "db.r5.xlarge", "finding": "Underutilized", "avg_cpu_utilization": 15.5, "recommendation": { "recommended_instance_class": "db.r5.large", "estimated_monthly_savings": 180.00 } } ], "count": 1, "total_monthly_savings": 180.00 }, "message": "Found 1 underutilized RDS instances" } enhanced_rds = add_documentation_links(rds_result, "rds") print(json.dumps(enhanced_rds, indent=2)) print("\n" + "=" * 60 + "\n") # Example 4: Lambda Optimization Analysis print("4. Lambda Optimization Analysis Output:") print("-" * 40) lambda_result = { "status": "success", "data": { "overprovisioned_functions": [ { "function_name": "data-processor", "current_memory": 1024, "avg_memory_utilization": 35.2, "recommendation": { "recommended_memory": 512, "estimated_monthly_savings": 25.75 } } ], "count": 1, "total_monthly_savings": 25.75 }, "message": "Found 1 overprovisioned Lambda functions" } enhanced_lambda = add_documentation_links(lambda_result, "lambda") print(json.dumps(enhanced_lambda, indent=2)) print("\n" + "=" * 60 + "\n") # Example 5: General Cost Analysis (no specific service) print("5. General Cost Analysis Output:") print("-" * 40) general_result = { "status": "success", "data": { "total_monthly_cost": 5420.75, "potential_savings": 1250.50, "services_analyzed": ["EC2", "EBS", "RDS", "Lambda", "S3"], "optimization_opportunities": 47 }, "message": "Comprehensive cost analysis completed" } enhanced_general = add_documentation_links(general_result) print(json.dumps(enhanced_general, indent=2)) print("\n" + "=" * 60 + "\n") print("Key Benefits of Documentation Links:") print("• Provides immediate access to AWS best practices") print("• Links to CFM-TIPs guidance and workshops") print("• References AWS Well-Architected Framework") print("• Service-specific playbooks for detailed guidance") print("• Consistent across all tool outputs") print("• Helps users understand optimization recommendations") if __name__ == "__main__": show_example_outputs()

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