Deploy on AWS, GCP, and Azure

FROM node:20-alpine

WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .

EXPOSE 3000
CMD ["node", "dist/server.js"]

# Authenticate with ECR
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <account-id>.dkr.ecr.us-east-1.amazonaws.com

# Build image
docker build -t my-mcp .

# Tag and push
docker tag my-mcp:latest <account-id>.dkr.ecr.us-east-1.amazonaws.com/my-mcp:latest
docker push <account-id>.dkr.ecr.us-east-1.amazonaws.com/my-mcp:latest

# Create task definition (task-definition.json)
aws ecs register-task-definition --cli-input-json file://task-definition.json

# Create service
aws ecs create-service \
  --cluster my-cluster \
  --service-name my-mcp-service \
  --task-definition my-mcp:1 \
  --desired-count 2 \
  --launch-type FARGATE

# Build and push to Artifact Registry
gcloud builds submit --tag gcr.io/PROJECT_ID/my-mcp

gcloud run deploy my-mcp \
  --image gcr.io/PROJECT_ID/my-mcp \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated \
  --memory 512Mi \
  --timeout 300

az acr create --resource-group myResourceGroup --name myregistry --sku Basic

az acr build --registry myregistry --image my-mcp:v1 .

az containerapp create \
  --name my-mcp \
  --resource-group myResourceGroup \
  --environment myEnvironment \
  --image myregistry.azurecr.io/my-mcp:v1 \
  --target-port 3000 \
  --ingress external

import { CloudWatchClient, PutMetricDataCommand } from "@aws-sdk/client-cloudwatch";

const cloudwatch = new CloudWatchClient({});

async function logToolCall(toolName: string, duration: number) {
  await cloudwatch.send(new PutMetricDataCommand({
    Namespace: "MCP/Tools",
    MetricData: [{
      MetricName: "Duration",
      Dimensions: [{ Name: "Tool", Value: toolName }],
      Value: duration,
      Unit: "Milliseconds"
    }]
  }));
}

const monitoring = require('@google-cloud/monitoring');
const client = new monitoring.MetricServiceClient();

// Similar setup for custom metrics