AWS Advisor MCP Server

Instructions

Input Schema

Input Schema (JSON Schema)

Implementation Reference

• src/aws_advisor_server.py:150-179 (handler)

  Executes the suggest_aws_service tool: validates input, calls search_aws_services helper, formats and returns markdown recommendations grouped by AWS service category.
  if name == "suggest_aws_service": use_case = arguments.get("use_case", "") if not use_case: return [TextContent( type="text", text="Error: Please provide a use case description." )] recommendations = search_aws_services(use_case) if not recommendations: return [TextContent( type="text", text=f"No specific AWS services found matching '{use_case}'. Try describing your use case differently (e.g., 'serverless compute', 'object storage', 'relational database')." )] # Format the response response_lines = [f"## AWS Service Recommendations for: '{use_case}'\n"] for category, services in recommendations.items(): response_lines.append(f"\n### {category.upper().replace('_', ' ')}") for service_name, description in services.items(): response_lines.append(f"\n**{service_name}**") response_lines.append(f"- {description}") return [TextContent( type="text", text="\n".join(response_lines) )]
• src/aws_advisor_server.py:122-135 (schema)

  Input schema definition for the suggest_aws_service tool: requires a 'use_case' string describing the requirements.
  Tool( name="suggest_aws_service", description="Get AWS service recommendations based on your use case. Provide a description of what you want to build or accomplish, and get relevant AWS service suggestions.", inputSchema={ "type": "object", "properties": { "use_case": { "type": "string", "description": "Description of your use case, requirements, or what you want to build (e.g., 'serverless API', 'store images', 'real-time data processing')" } }, "required": ["use_case"] } ),
• src/aws_advisor_server.py:118-144 (registration)

  Registers the suggest_aws_service tool via the MCP server's list_tools handler, including its schema and description.
  @server.list_tools() async def list_tools() -> list[Tool]: """List available tools.""" return [ Tool( name="suggest_aws_service", description="Get AWS service recommendations based on your use case. Provide a description of what you want to build or accomplish, and get relevant AWS service suggestions.", inputSchema={ "type": "object", "properties": { "use_case": { "type": "string", "description": "Description of your use case, requirements, or what you want to build (e.g., 'serverless API', 'store images', 'real-time data processing')" } }, "required": ["use_case"] } ), Tool( name="list_aws_categories", description="List all available AWS service categories (compute, storage, database, etc.) to help you explore services by type.", inputSchema={ "type": "object", "properties": {} } ) ]
• src/aws_advisor_server.py:87-111 (helper)

  Core helper function that keyword-matches the use_case against AWS service descriptions in AWS_SERVICES dict to produce category-grouped recommendations.
  def search_aws_services(use_case: str) -> dict[str, Any]: """ Search for AWS services matching a use case description. Args: use_case: Description of the use case or requirements Returns: Dictionary with recommended services and explanations """ use_case_lower = use_case.lower() recommendations = {} # Search through all services for category, services in AWS_SERVICES.items(): matching_services = {} for service_name, description in services.items(): # Check if use case keywords match service description if any(word in description.lower() for word in use_case_lower.split()): matching_services[service_name] = description if matching_services: recommendations[category] = matching_services return recommendations
• src/aws_advisor_server.py:16-84 (helper)

  Static data structure containing AWS services organized by category with descriptive use cases for keyword matching.
  AWS_SERVICES = { "compute": { "EC2": "Virtual servers for flexible compute capacity, long-running applications", "Lambda": "Serverless functions for event-driven, short-duration workloads", "ECS": "Container orchestration with Docker, managed container service", "EKS": "Managed Kubernetes for container orchestration at scale", "Fargate": "Serverless container compute, no server management", "Lightsail": "Simple virtual private servers for small projects, pre-configured apps" }, "storage": { "S3": "Object storage for files, backups, static websites, data lakes", "EBS": "Block storage volumes for EC2 instances, database storage", "EFS": "Managed file storage for Linux workloads, shared file systems", "Glacier": "Long-term archival storage, compliance, cold data", "FSx": "High-performance file systems (Windows, Lustre, NetApp, OpenZFS)" }, "database": { "RDS": "Managed relational databases (MySQL, PostgreSQL, Oracle, SQL Server)", "DynamoDB": "NoSQL key-value database, high performance, serverless", "Aurora": "MySQL/PostgreSQL compatible, high-performance relational database", "DocumentDB": "MongoDB-compatible document database", "ElastiCache": "In-memory caching (Redis, Memcached), session storage", "Neptune": "Graph database for connected data, social networks, recommendations", "Redshift": "Data warehouse for analytics, OLAP, business intelligence" }, "networking": { "VPC": "Virtual private cloud, network isolation, custom networking", "CloudFront": "CDN for content delivery, low latency, global distribution", "Route53": "DNS service, domain registration, traffic routing", "API Gateway": "Create, publish, maintain APIs, REST/WebSocket APIs", "Direct Connect": "Dedicated network connection from on-premises to AWS", "ELB": "Load balancing (Application, Network, Gateway Load Balancers)" }, "analytics": { "Athena": "Query S3 data with SQL, serverless analytics", "EMR": "Big data processing (Hadoop, Spark), data transformation", "Kinesis": "Real-time data streaming, log analytics, IoT data", "Glue": "ETL service, data cataloging, data preparation", "QuickSight": "Business intelligence, dashboards, visualizations" }, "ml_ai": { "SageMaker": "Build, train, deploy ML models, managed Jupyter notebooks", "Rekognition": "Image and video analysis, face detection, content moderation", "Comprehend": "Natural language processing, sentiment analysis, entity extraction", "Polly": "Text-to-speech, voice synthesis", "Transcribe": "Speech-to-text, audio transcription", "Translate": "Language translation service", "Lex": "Conversational interfaces, chatbots, voice assistants" }, "security": { "IAM": "Identity and access management, user permissions, roles", "Cognito": "User authentication, identity pools, user management", "KMS": "Key management, encryption keys, cryptographic operations", "Secrets Manager": "Store and rotate secrets, credentials, API keys", "WAF": "Web application firewall, DDoS protection, traffic filtering", "GuardDuty": "Threat detection, security monitoring, anomaly detection" }, "messaging": { "SQS": "Message queuing, decouple services, asynchronous processing", "SNS": "Pub/sub messaging, notifications, mobile push, email/SMS", "EventBridge": "Event bus, application integration, serverless workflows", "SES": "Email sending service, transactional emails, marketing campaigns" }, "monitoring": { "CloudWatch": "Monitoring, logs, metrics, alarms, dashboards", "X-Ray": "Distributed tracing, application debugging, performance analysis", "CloudTrail": "API logging, governance, compliance, audit trails" } }