> DEPLOYMENT & PERFORMANCE [SCALE READY]

▶ PRODUCTION DEPLOYMENT ARCHITECTURE

Async I/O, graceful shutdowns, containerization with resource limits

┌──────────────────────────────────────────────────────────────────┐ │ PRODUCTION DEPLOYMENT STACK │ ├──────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ LOAD │────►│ MCP SERVER │────►│ RESOURCES │ │ │ │ BALANCER │ │ CONTAINER │ │ (DB/FILES) │ │ │ │ (nginx) │ │ (Docker) │ │ │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ TLS TERM │ │ RESOURCE │ │ MONITORING │ │ │ │ + AUTH │ │ LIMITS │ │ & LOGGING │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ │ Horizontal scaling with multiple container instances │ └──────────────────────────────────────────────────────────────────┘

▶ ASYNC I/O AND CONCURRENCY

import asyncio
from typing import AsyncGenerator

@server.tool()
async def process_large_dataset(data_source: str) -> AsyncGenerator[str, None]:
    """Process large dataset with streaming results."""

    # Async file processing
    async with aiofiles.open(data_source, 'r') as file:
        async for chunk in file:
            # Non-blocking processing
            result = await process_chunk_async(chunk)

            # Stream results back
            yield f"Processed: {result}"

            # Yield control to event loop
            await asyncio.sleep(0)

# Concurrent tool execution
async def handle_multiple_requests():
    tasks = [
        process_tool_a(),
        process_tool_b(),
        process_tool_c()
    ]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return results

▶ GRACEFUL SHUTDOWN PATTERNS

🛑 SIGNAL HANDLING

SIGTERM, SIGINT for clean shutdown

⏳ REQUEST COMPLETION

Wait for active requests to finish

🔄 RESOURCE CLEANUP

Close connections, flush buffers, save state

🎮 LIVE DEMO: RESOURCE SCOPING

DEMONSTRATION: listRoots shows file scoping and safe enumeration

$ mcp-inspector
> Call listRoots tool

Response: {
  "roots": [
    {
      "uri": "file:///workspace/mcp-demo",
      "name": "MCP Demo Project",
      "description": "Demo project files and resources"
    },
    {
      "uri": "file:///shared/templates",
      "name": "Shared Templates",
      "description": "Read-only template library"
    }
  ],
  "access_policy": {
    "read": ["*.md", "*.json", "*.yaml"],
    "write": ["workspace/**/*"],
    "exclude": [".env", "credentials.*", "*.key"]
  }
}

[SECURITY] Only listed roots accessible
[PERFORMANCE] Efficient directory enumeration
[AUDIT] All access attempts logged

Performance Optimizations:

Lazy Loading: Resources loaded on demand
Caching Strategy: Frequently accessed data cached
Batch Operations: Multiple file operations combined
Index Optimization: Fast directory traversal

▶ CONTAINERIZED DEPLOYMENT

# Production-ready MCP server container
FROM python:3.11-slim

# Security: non-root user
RUN useradd -m -u 1000 mcpuser

# Performance: multi-stage build for smaller image
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Resource limits in docker-compose
services:
  mcp-server:
    build: .
    user: "1000:1000"
    deploy:
      resources:
        limits:
          cpus: '2.0'
          memory: 1G
        reservations:
          cpus: '0.5'
          memory: 256M
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

▶ MONITORING & OBSERVABILITY

📊 METRICS COLLECTION

Response times, throughput, error rates, resource usage

🔍 DISTRIBUTED TRACING

Request flows across multiple servers

📈 PERFORMANCE DASHBOARDS

Real-time operational insights

🚨 ALERTING

Automated notifications for anomalies

▶ HORIZONTAL SCALING STRATEGIES

# Kubernetes deployment example
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mcp-server
spec:
  replicas: 3
  selector:
    matchLabels:
      app: mcp-server
  template:
    spec:
      containers:
      - name: mcp-server
        image: mcp-server:latest
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000

Scaling Considerations:

Stateless Design: Servers can be replicated easily
Load Distribution: Round-robin or intelligent routing
Health Checks: Automatic failover and recovery
Rolling Updates: Zero-downtime deployments