Cost-Efficient MCP Server Deployment on AWS for AI Agents

A comprehensive guide to deploying Model Context Protocol (MCP) servers on AWS services cost-effectively and integrating them with AI agent frameworks like Google ADK.

Table of Contents

1. What is MCP? (Start Here)
2. Prerequisites
3. Quick Start Guide
4. MCP Architecture Fundamentals
5. AWS Deployment Options
6. Cost Comparison Analysis
7. Recommended Deployment Strategies
8. Step-by-Step Implementation
9. Google ADK Integration
10. Troubleshooting
11. Monitoring and Optimization
12. Best Practices
13. Resources

What is MCP? (Start Here)

Model Context Protocol (MCP) is like a universal translator that allows AI applications (like Claude, ChatGPT, or Google's AI agents) to connect to external tools and data sources in a standardized way.

Think of it this way:

• Without MCP: Each AI tool needs custom code to connect to databases, APIs, or services
• With MCP: AI tools use a standard "language" to talk to any MCP server, regardless of what it connects to

Real-World Example:

Instead of building custom integrations for every tool, you create one MCP server that can:

• Query your company's database
• Call external APIs
• Analyze files
• Perform calculations

Then any MCP-compatible AI agent can use all these capabilities instantly.

Key Benefits of MCP

• Plug-and-Play: Connect any AI agent to any tool through a standard interface
• Real-Time Communication: Live data exchange with Server-Sent Events (SSE)
• Secure and Auditable: Built-in access control and comprehensive logging
• Highly Extensible: Easy to add new capabilities without changing AI agent code
• Cost-Effective: Share one MCP server across multiple AI agents and applications

Prerequisites

Before you begin, ensure you have:

Required Knowledge

• Basic understanding of AWS services (Lambda, EC2, or containers)
• Python programming experience
• Basic command line usage
• Understanding of APIs and JSON

AWS Account Setup

• Active AWS account with billing enabled
• AWS CLI installed and configured
• Basic IAM permissions for Lambda/Fargate deployment

Development Environment

• Python 3.9+ installed
• Node.js 18+ (for CDK examples)
• Docker (for containerized deployments)

Estimated Time Investment

• Quick Start: 30 minutes
• Full Implementation: 2-4 hours
• Production Deployment: 1-2 days

Quick Start Guide

Step 1: Choose Your Deployment Method (5 minutes)

For Beginners: Start with AWS Lambda (pay-per-use, no servers to manage)For Production: Consider AWS Fargate (always-on, more predictable costs)

Step 2: Quick Decision Tree

Do you expect < 1 million requests per month?
├─ YES → Use AWS Lambda ($10-50/month)
└─ NO → Do you need 24/7 availability?
   ├─ YES → Use AWS Fargate ($50-200/month)
   └─ NO → Use AWS Lambda with provisioned concurrency

Step 3: Your First MCP Server (15 minutes)

We'll create a simple MCP server that can analyze text and get current weather data.

Step 4: Test and Deploy (10 minutes)

Test locally, then deploy to AWS with a single command.

MCP Architecture Fundamentals

flowchart LR subgraph "Your Computer" Host["Host with MCP Client\n(Claude, IDEs, Tools)"] S1["MCP Server A"] S2["MCP Server B"] S3["MCP Server C"] Host <-->|"MCP Protocol"| S1 Host <-->|"MCP Protocol"| S2 Host <-->|"MCP Protocol"| S3 S1 <--> D1[("Local\nData Source A")] S2 <--> D2[("Local\nData Source B")] end subgraph "Internet" S3 <-->|"Web APIs"| D3[("Remote\nService C")] end

Core Components

• MCP Host: The LLM-powered application (Claude, Google ADK agents)
• MCP Client: Maintains 1:1 connection with MCP Server
• MCP Server: Supplies context, tools, and prompts to the client

AWS Deployment Options

1. AWS Lambda (Serverless) - RECOMMENDED FOR COST EFFICIENCY

Pros:

• Pay only for execution time (no idle costs)
• Automatic scaling
• No server management
• Ideal for variable/unpredictable workloads
• Cold start optimizations available

Cons:

• 15-minute execution limit
• Cold start latency (100ms-5 seconds)
• Limited to 10GB memory
• Complex for long-running processes

Best For:

• Event-driven MCP servers
• Sporadic usage patterns
• Cost-sensitive deployments
• Quick prototyping

2. AWS Fargate (Container-as-a-Service)

Pros:

• No execution time limits
• Better for long-running processes
• More control over runtime environment
• No cold starts once running
• Up to 120GB memory, 16 vCPU

Cons:

• Higher costs due to continuous resource allocation
• Longer startup times (35 seconds - 2 minutes)
• More complex setup
• Pay for allocated resources even if idle

Best For:

• Enterprise deployments requiring high availability
• Long-running MCP servers
• Complex security requirements
• Consistent workloads

3. Amazon ECS on EC2

Pros:

• Maximum control and customization
• Cost-effective for consistent high usage
• Can use Reserved Instances for savings

Cons:

• Requires server management
• Higher operational overhead
• Not truly serverless

Best For:

• High-volume, consistent workloads
• Custom infrastructure requirements

Cost Comparison Analysis

Lambda Pricing Model

Cost = (Number of Requests × $0.20 per 1M requests) + 
       (Duration × Memory × $0.0001667 per GB-second)

Example: 1M requests/month, 1GB memory, 2-second average duration

• Requests: 1M × $0.20/1M = $0.20
• Compute: 1M × 2s × 1GB × $0.0001667 = $333.40
• Total: ~$333.60/month

Fargate Pricing Model

Cost = (vCPU hours × $0.04048) + (GB memory hours × $0.004445)

Example: 1 vCPU, 2GB memory, running 24/7

• vCPU: 744 hours × $0.04048 = $30.12
• Memory: 744 hours × 2GB × $0.004445 = $6.61
• Total: ~$36.73/month

Cost Optimization Recommendations

Usage Pattern	Recommended Service	Estimated Monthly Cost	Best For
< 100K requests/month, testing	Lambda	$2-10	Prototyping, learning
100K-1M requests/month, sporadic	Lambda	$10-50	Small apps, personal projects
1-10M requests/month, variable	Lambda	$50-200	Growing applications
> 10M requests/month, consistent	Fargate	$100-500	Stable production workloads
Enterprise, 24/7 availability	Fargate + Load Balancer	$500-2000	Mission-critical systems

Quick Reference: When to Use What

🟢 USE LAMBDA WHEN:
✓ You're starting out or prototyping
✓ Traffic is unpredictable or sporadic  
✓ You want minimal operational overhead
✓ Cost optimization is priority #1
✓ You can handle 100ms-5s cold starts

🟡 USE FARGATE WHEN:
✓ You need consistent sub-100ms response times
✓ Traffic is predictable and consistent
✓ You're running 24/7 workloads
✓ You need complex networking or security
✓ You have containerized applications

Step-by-Step Implementation

Phase 1: Create Your First MCP Server (Local Development)

1. Set up the project structure

mkdir my-mcp-server cd my-mcp-server

Create virtual environment

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Install dependencies

pip install mcp anthropic-mcp-server boto3

2. Create a simple MCP server (mcp_server.py)

#!/usr/bin/env python3 """ Simple MCP Server for AWS - Beginner Example This server provides basic text analysis and AWS cost checking tools """

import asyncio import logging from mcp.server import Server from mcp.server.stdio import stdio_server import mcp.types as types

Configure logging

logging.basicConfig(level=logging.INFO) logger = logging.getLogger(name)

Create the MCP server

app = Server("my-first-mcp-server")

@app.list_tools() async def list_tools() -> list[types.Tool]: """List available tools for the MCP client""" return [ types.Tool( name="text-analyzer", description="Analyze text for word count, sentiment, and key phrases", inputSchema={ "type": "object", "properties": { "text": { "type": "string", "description": "Text to analyze" }, "analysis_type": { "type": "string", "enum": ["word_count", "sentiment", "summary"], "description": "Type of analysis to perform" } }, "required": ["text", "analysis_type"] } ), types.Tool( name="aws-simple-cost-check", description="Get basic AWS cost information (demo version)", inputSchema={ "type": "object", "properties": { "service": { "type": "string", "description": "AWS service to check (e.g., 'lambda', 'ec2')" } }, "required": ["service"] } ) ]

@app.call_tool() async def call_tool(name: str, arguments: dict) -> list[types.TextContent]: """Handle tool calls from the MCP client"""

if name == "text-analyzer":
    text = arguments["text"]
    analysis_type = arguments["analysis_type"]
    
    if analysis_type == "word_count":
        word_count = len(text.split())
        char_count = len(text)
        result = f"Word count: {word_count}\nCharacter count: {char_count}"
        
    elif analysis_type == "sentiment":
        # Simple sentiment analysis (in production, use a proper NLP library)
        positive_words = ["good", "great", "excellent", "amazing", "wonderful"]
        negative_words = ["bad", "terrible", "awful", "horrible", "disappointing"]
        
        text_lower = text.lower()
        positive_count = sum(1 for word in positive_words if word in text_lower)
        negative_count = sum(1 for word in negative_words if word in text_lower)
        
        if positive_count > negative_count:
            sentiment = "Positive"
        elif negative_count > positive_count:
            sentiment = "Negative"
        else:
            sentiment = "Neutral"
            
        result = f"Sentiment: {sentiment}\nPositive indicators: {positive_count}\nNegative indicators: {negative_count}"
        
    elif analysis_type == "summary":
        sentences = text.split('. ')
        result = f"Text summary:\n- Total sentences: {len(sentences)}\n- First sentence: {sentences[0] if sentences else 'No sentences found'}"
    
    return [types.TextContent(type="text", text=result)]

elif name == "aws-simple-cost-check":
    service = arguments["service"]
    
    # Mock cost data (in production, use boto3 and AWS Cost Explorer)
    mock_costs = {
        "lambda": "$12.50 this month (estimated)",
        "ec2": "$45.30 this month (estimated)", 
        "s3": "$8.75 this month (estimated)"
    }
    
    cost_info = mock_costs.get(service.lower(), "Cost data not available for this service")
    result = f"AWS {service.upper()} costs: {cost_info}"
    
    return [types.TextContent(type="text", text=result)]

raise ValueError(f"Unknown tool: {name}")

async def main(): """Run the MCP server""" logger.info("Starting MCP server...")

async with stdio_server() as streams:
    await app.run(
        streams[0], streams[1], app.create_initialization_options()
    )

if name == "main": asyncio.run(main())

3. Test your MCP server locally

Test the server

python mcp_server.py

In another terminal, you can test with:

echo '{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}' | python mcp_server.py

Phase 2: Deploy to AWS Lambda (Production Ready)

1. Create deployment package

Create requirements.txt

cat > requirements.txt << EOF mcp>=1.0.0 boto3>=1.26.0 anthropic-mcp-server>=0.1.0 EOF

Create lambda deployment package

mkdir lambda_package pip install -r requirements.txt -t lambda_package/ cp mcp_server.py lambda_package/

2. Create Lambda-compatible handler (lambda_package/lambda_handler.py)

import json import asyncio import logging from mcp_server import app

logger = logging.getLogger() logger.setLevel(logging.INFO)

def lambda_handler(event, context): """AWS Lambda handler for MCP server"""

try:
    # Extract MCP request from Lambda event
    if 'body' in event:
        # API Gateway event
        mcp_request = json.loads(event['body'])
    else:
        # Direct Lambda invocation
        mcp_request = event
    
    # Process MCP request
    response = asyncio.run(process_mcp_request(mcp_request))
    
    return {
        'statusCode': 200,
        'headers': {
            'Content-Type': 'application/json',
            'Access-Control-Allow-Origin': '*'
        },
        'body': json.dumps(response)
    }
    
except Exception as e:
    logger.error(f"Error processing MCP request: {str(e)}")
    return {
        'statusCode': 500,
        'body': json.dumps({'error': str(e)})
    }

async def process_mcp_request(request): """Process MCP request and return response"""

method = request.get('method')
params = request.get('params', {})
request_id = request.get('id')

try:
    if method == 'tools/list':
        tools = await app.list_tools()
        result = [tool.model_dump() for tool in tools]
        
    elif method == 'tools/call':
        tool_name = params.get('name')
        arguments = params.get('arguments', {})
        result = await app.call_tool(tool_name, arguments)
        result = [content.model_dump() for content in result]
        
    else:
        raise ValueError(f"Unknown method: {method}")
    
    return {
        'jsonrpc': '2.0',
        'id': request_id,
        'result': result
    }
    
except Exception as e:
    return {
        'jsonrpc': '2.0',
        'id': request_id,
        'error': {
            'code': -32603,
            'message': str(e)
        }
    }

3. Deploy with AWS CLI

Create deployment zip

cd lambda_package zip -r ../mcp-server-lambda.zip . cd ..

Create Lambda function

aws lambda create-function
--function-name my-mcp-server
--runtime python3.11
--role arn:aws:iam::YOUR-ACCOUNT:role/lambda-execution-role
--handler lambda_handler.lambda_handler
--zip-file fileb://mcp-server-lambda.zip
--timeout 300
--memory-size 512

Phase 3: Connect to AI Agents

1. Test with Claude Desktop

Add to your Claude Desktop config:

{ "mcpServers": { "my-mcp-server": { "command": "python", "args": ["/path/to/your/mcp_server.py"] } } }

2. Connect via HTTP API

import requests

def call_mcp_server(tool_name, arguments): """Call MCP server via HTTP API"""

payload = {
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": tool_name,
        "arguments": arguments
    }
}

response = requests.post(
    "YOUR-LAMBDA-API-URL",
    json=payload,
    headers={"Content-Type": "application/json"}
)

return response.json()

Example usage

result = call_mcp_server("text-analyzer", { "text": "This is a great example of MCP!", "analysis_type": "sentiment" }) print(result)

Recommended Deployment Strategies

Strategy 1: Pure Lambda Serverless (Most Cost-Efficient)

Basic MCP Server on Lambda

import json import asyncio from mcp.server import Server from mcp.server.stdio import stdio_server import mcp.types as types

app = Server("aws-lambda-mcp-server")

@app.list_tools() async def list_tools() -> list[types.Tool]: return [ types.Tool( name="aws-cost-analyzer", description="Analyze AWS costs and usage", inputSchema={ "type": "object", "properties": { "service": {"type": "string"}, "region": {"type": "string"} } } ) ]

@app.call_tool() async def call_tool(name: str, arguments: dict) -> list[types.TextContent]: if name == "aws-cost-analyzer": # Implement cost analysis logic result = analyze_aws_costs(arguments) return [types.TextContent(type="text", text=result)]

raise ValueError(f"Unknown tool: {name}")

def lambda_handler(event, context): # Lambda handler implementation return asyncio.run(handle_mcp_request(event))

Strategy 2: Fargate for Enterprise (High Availability)

docker-compose.yml for Fargate deployment

version: '3.8' services: mcp-server: build: . ports: - "8080:8080" environment: - AWS_REGION=us-east-1 - LOG_LEVEL=INFO healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8080/health"] interval: 30s timeout: 10s retries: 3

Strategy 3: Hybrid Approach

• Lambda: For lightweight, event-driven MCP tools
• Fargate: For resource-intensive, long-running MCP servers
• API Gateway: For HTTP-based MCP server endpoints

Troubleshooting

Common Issues and Solutions

1. "Module 'mcp' not found"

Problem: Python can't find the MCP librarySolution:

Make sure you're in the virtual environment

source venv/bin/activate

Install the correct package

pip install mcp>=1.0.0

Alternative: Try the anthropic package

pip install anthropic-mcp-server

2. Lambda deployment fails with "Role does not exist"

Problem: Missing IAM role for Lambda executionSolution:

Create basic Lambda execution role

aws iam create-role
--role-name lambda-execution-role
--assume-role-policy-document '{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": {"Service": "lambda.amazonaws.com"}, "Action": "sts:AssumeRole" } ] }'

Attach basic execution policy

aws iam attach-role-policy
--role-name lambda-execution-role
--policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

3. MCP server responds with "Unknown method"

Problem: Client is sending unsupported MCP methodSolution: Check that you're implementing the required MCP methods:

Required methods for basic MCP server

@app.list_tools() # For tools/list @app.call_tool() # For tools/call
@app.list_resources() # Optional: for resources/list @app.list_prompts() # Optional: for prompts/list

4. AWS API permissions errors

Problem: Lambda can't access AWS services (S3, DynamoDB, etc.)Solution: Add IAM permissions to your Lambda role:

Example: Add S3 access

aws iam attach-role-policy
--role-name lambda-execution-role
--policy-arn arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess

5. Cold start timeouts

Problem: Lambda takes too long to startSolutions:

• Reduce package size (remove unnecessary dependencies)
• Use provisioned concurrency for critical functions
• Consider moving to Fargate for consistent performance

6. MCP client can't connect to server

Problem: Connection issues between client and serverDebugging steps:

Test MCP server locally

python mcp_server.py

Test with curl (for HTTP endpoints)

curl -X POST your-api-endpoint
-H "Content-Type: application/json"
-d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Check CloudWatch logs for Lambda

aws logs describe-log-groups --log-group-name-prefix /aws/lambda/my-mcp-server

Performance Optimization Tips

1. Reduce Cold Starts

Initialize expensive resources outside the handler

import boto3

Global initialization (runs once per container)

s3_client = boto3.client('s3') dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context): # Handler runs for each request # Use pre-initialized clients pass

2. Implement Caching

import json from functools import lru_cache

@lru_cache(maxsize=100) def expensive_operation(input_data): """Cache expensive operations""" # Your expensive logic here return result

3. Use Connection Pooling

import boto3 from botocore.config import Config

Configure connection pooling

config = Config( max_pool_connections=10, retries={'max_attempts': 3} )

client = boto3.client('dynamodb', config=config)

Debugging Checklist

• Virtual environment activated
• All dependencies installed
• AWS credentials configured
• IAM roles and policies set up
• MCP server responds to basic methods
• CloudWatch logs enabled
• API Gateway configured (if using HTTP)
• Client configuration matches server endpoint

Google ADK Integration

Architecture Overview

flowchart TD A[Google ADK Agent] --> B[MCP Client] B --> C[AWS Lambda MCP Server] B --> D[AWS Fargate MCP Server] C --> E[AWS Services] D --> F[External APIs] E --> G[DynamoDB] E --> H[S3] E --> I[CloudWatch]

ADK Agent Configuration

Google ADK agent with MCP integration

from google.adk import Agent, Tool from mcp_client import MCPClient

class AWSMCPTool(Tool): def init(self): self.mcp_client = MCPClient() super().init( name="aws-mcp-tool", description="Access AWS services via MCP server" )

async def execute(self, **kwargs):
    # Connect to MCP server on AWS
    await self.mcp_client.connect("https://your-mcp-server.amazonaws.com")
    
    # Execute tool via MCP
    result = await self.mcp_client.call_tool(
        name="aws-cost-analyzer",
        arguments=kwargs
    )
    
    return result

ADK Agent setup

agent = Agent( name="AWS Cost Assistant", model="gemini-2.0-flash-exp", tools=[AWSMCPTool()], instructions="You are an AWS cost optimization assistant..." )

Multi-Agent Collaboration

Multi-agent setup with MCP

from google.adk.a2a import AgentCommunication

Cost Analysis Agent

cost_agent = Agent( name="Cost Analyzer", tools=[AWSCostMCPTool()], instructions="Analyze AWS costs and identify optimization opportunities" )

Resource Optimization Agent

optimize_agent = Agent( name="Resource Optimizer", tools=[AWSResourceMCPTool()], instructions="Implement cost optimization recommendations" )

A2A Communication

comm = AgentCommunication([cost_agent, optimize_agent])

Workflow

async def cost_optimization_workflow(query): # Step 1: Analyze costs analysis = await cost_agent.process(query)

# Step 2: Get optimization recommendations
recommendations = await optimize_agent.process(analysis)

return recommendations

Implementation Examples

1. AWS Lambda MCP Server with CDK

// CDK Infrastructure import * as cdk from 'aws-cdk-lib'; import * as lambda from 'aws-cdk-lib/aws-lambda'; import * as apigateway from 'aws-cdk-lib/aws-apigateway';

export class MCPServerStack extends cdk.Stack { constructor(scope: Construct, id: string, props?: cdk.StackProps) { super(scope, id, props);

// Lambda function for MCP server
const mcpServer = new lambda.Function(this, 'MCPServer', {
  runtime: lambda.Runtime.PYTHON_3_11,
  handler: 'mcp_server.lambda_handler',
  code: lambda.Code.fromAsset('src'),
  timeout: cdk.Duration.minutes(15),
  memorySize: 1024,
  environment: {
    LOG_LEVEL: 'INFO'
  }
});

// API Gateway for HTTP access
const api = new apigateway.RestApi(this, 'MCPServerAPI', {
  restApiName: 'MCP Server Service'
});

const integration = new apigateway.LambdaIntegration(mcpServer);
api.root.addMethod('POST', integration);

} }

2. Fargate MCP Server with High Availability

AWS ECS Task Definition

version: '1' taskDefinition: family: mcp-server networkMode: awsvpc requiresCompatibilities: - FARGATE cpu: '256' memory: '512' containerDefinitions: - name: mcp-server image: your-account.dkr.ecr.region.amazonaws.com/mcp-server:latest portMappings: - containerPort: 8080 protocol: tcp environment: - name: AWS_REGION value: us-east-1 logConfiguration: logDriver: awslogs options: awslogs-group: /ecs/mcp-server awslogs-region: us-east-1 awslogs-stream-prefix: ecs

3. Cost Optimization MCP Server

AWS Cost Analysis MCP Server

import boto3 from datetime import datetime, timedelta from mcp.server import Server import mcp.types as types

app = Server("aws-cost-mcp-server")

@app.list_tools() async def list_tools(): return [ types.Tool( name="get-cost-breakdown", description="Get AWS cost breakdown by service", inputSchema={ "type": "object", "properties": { "days": {"type": "integer", "default": 30}, "granularity": {"type": "string", "enum": ["DAILY", "MONTHLY"]} } } ), types.Tool( name="identify-unused-resources", description="Identify unused AWS resources", inputSchema={ "type": "object", "properties": { "services": {"type": "array", "items": {"type": "string"}} } } ) ]

@app.call_tool() async def call_tool(name: str, arguments: dict): ce_client = boto3.client('ce') # Cost Explorer

if name == "get-cost-breakdown":
    end_date = datetime.now()
    start_date = end_date - timedelta(days=arguments.get('days', 30))
    
    response = ce_client.get_cost_and_usage(
        TimePeriod={
            'Start': start_date.strftime('%Y-%m-%d'),
            'End': end_date.strftime('%Y-%m-%d')
        },
        Granularity=arguments.get('granularity', 'MONTHLY'),
        Metrics=['BlendedCost'],
        GroupBy=[{'Type': 'DIMENSION', 'Key': 'SERVICE'}]
    )
    
    # Process and format results
    cost_breakdown = format_cost_data(response)
    
    return [types.TextContent(
        type="text",
        text=f"AWS Cost Breakdown:\n{cost_breakdown}"
    )]

elif name == "identify-unused-resources":
    # Implement unused resource identification
    unused_resources = find_unused_resources(arguments.get('services', []))
    
    return [types.TextContent(
        type="text", 
        text=f"Unused Resources Found:\n{unused_resources}"
    )]

Monitoring and Optimization

CloudWatch Dashboards

{ "widgets": [ { "type": "metric", "properties": { "metrics": [ ["AWS/Lambda", "Duration", "FunctionName", "mcp-server"], ["AWS/Lambda", "Invocations", "FunctionName", "mcp-server"], ["AWS/Lambda", "Errors", "FunctionName", "mcp-server"] ], "period": 300, "stat": "Average", "region": "us-east-1", "title": "MCP Server Lambda Metrics" } } ] }

Cost Alerts

CloudWatch Cost Alarm

import boto3

cloudwatch = boto3.client('cloudwatch')

cloudwatch.put_metric_alarm( AlarmName='MCP-Server-High-Cost', ComparisonOperator='GreaterThanThreshold', EvaluationPeriods=1, MetricName='EstimatedCharges', Namespace='AWS/Billing', Period=86400, Statistic='Maximum', Threshold=100.0, ActionsEnabled=True, AlarmActions=[ 'arn:aws:sns:us-east-1:123456789012:billing-alerts' ], AlarmDescription='Alert when MCP server costs exceed $100' )

Best Practices

1. Cost Optimization

• Use Lambda for sporadic workloads (< 1M requests/month)
• Use Fargate for consistent workloads (> 10M requests/month)
• Implement proper caching to reduce redundant API calls
• Use AWS Savings Plans for predictable workloads
• Monitor and set cost alerts

2. Performance Optimization

• Minimize Lambda cold starts with provisioned concurrency
• Use connection pooling for database connections
• Implement proper error handling and retries
• Use CloudWatch insights for performance monitoring

3. Security Best Practices

• Use IAM roles with least privilege principle
• Enable VPC endpoints for secure AWS service access
• Implement proper authentication for MCP endpoints
• Use AWS WAF for API protection
• Enable CloudTrail for audit logging

4. Scalability Considerations

• Design for horizontal scaling
• Use SQS for async processing
• Implement circuit breakers for external API calls
• Use Auto Scaling for Fargate deployments

Resources

Official Documentation

• Model Context Protocol Specification
• AWS Lambda Documentation
• AWS Fargate Documentation
• Google ADK Documentation

Sample Implementations

• AWS Labs MCP Servers
• MCP Python SDK
• AWS Serverless MCP Examples

Cost Calculators

• AWS Lambda Pricing Calculator
• AWS Fargate Pricing Calculator

Community Resources

• MCP Registry
• AWS Serverless Patterns

---

Migration Path: From Prototype to Production

Phase 1: Development (Week 1)

• Start with: Local MCP server development
• Focus on: Core functionality and tool implementation
• Cost: $0 (local development only)

Phase 2: Testing (Week 2)

• Deploy to: AWS Lambda (basic setup)
• Focus on: Integration testing and debugging
• Cost: $5-20/month (low usage)

Phase 3: Production (Month 2)

• Scale to: Lambda with API Gateway OR Fargate
• Focus on: Performance optimization and monitoring
• Cost: $50-500/month (depending on usage)

Phase 4: Enterprise (Month 3+)

• Upgrade to: Multi-region Fargate with load balancing
• Focus on: High availability and advanced features
• Cost: $500-2000+/month (full enterprise features)

Conclusion

For Beginners: Start Simple

1. Follow the Step-by-Step Guide above to create your first MCP server locally
2. Deploy to Lambda for immediate cost savings and simplicity
3. Test with Claude Desktop or simple HTTP calls
4. Iterate and improve based on your specific needs

For Production: Choose Based on Usage

If you have...	Choose...	Because...
< 1M requests/month	AWS Lambda	Most cost-effective, automatic scaling
> 10M requests/month	AWS Fargate	Better performance, no cold starts
Enterprise requirements	Fargate + Load Balancer	High availability, better SLOs
Unpredictable traffic	Lambda with auto-scaling	Pay only for what you use

Key Success Factors

• Start Small: Begin with the basic example and expand gradually
• Monitor Costs: Set up billing alerts from day one
• Test Locally First: Debug issues locally before deploying to AWS
• Use the Troubleshooting Guide: Most issues have known solutions
• Plan for Growth: Design with future scaling in mind

Next Steps

1. Clone the example code and run it locally
2. Deploy your first Lambda function following the guide
3. Connect it to your favorite AI agent (Claude, Google ADK)
4. Monitor usage and costs for the first month
5. Scale based on actual usage patterns

The combination of MCP's standardization and AWS's cost-effective deployment options makes it easier than ever to build powerful AI agent integrations. Start with the basics, learn from real usage, and scale as needed.