Scaling Your Application

Learn how to scale your MCP Server integrations to handle high traffic and complex workloads.

Performance Optimization

Caching

Implement caching to reduce API calls:

import { MCPClient } from '@mcpserver/sdk';
import Redis from 'ioredis';

const redis = new Redis(process.env.REDIS_URL);
const client = new MCPClient({
  apiKey: process.env.MCP_API_KEY,
  cache: {
    enabled: true,
    adapter: redis,
    ttl: 3600, // 1 hour
  },
});

Connection Pooling

Use connection pooling for database queries:

await client.databases.connect({
  type: 'postgresql',
  connection: {
    host: process.env.DB_HOST,
    database: process.env.DB_NAME,
    user: process.env.DB_USER,
    password: process.env.DB_PASSWORD,
  },
  pool: {
    min: 2,
    max: 10,
  },
});

Batch Operations

Process multiple operations in batches:

const results = await client.batch([
  { tool: 'database-query', params: { sql: 'SELECT * FROM users' } },
  { tool: 'api-call', params: { endpoint: 'get-orders' } },
  { tool: 'file-read', params: { path: '/data/config.json' } },
]);

Load Balancing

Horizontal Scaling

Deploy multiple instances of your application:

docker-compose.yml
version: '3.8'
services:
  app:
    image: my-app:latest
    deploy:
      replicas: 3
    environment:
    - MCP_API_KEY=${MCP_API_KEY}

Auto-Scaling

Configure auto-scaling based on metrics:

kubernetes deployment
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: mcp-app
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: mcp-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
- type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Monitoring

Metrics Collection

Track key performance metrics:

const client = new MCPClient({
  apiKey: process.env.MCP_API_KEY,
  monitoring: {
    enabled: true,
    metrics: ['latency', 'throughput', 'errors'],
    reporter: 'prometheus',
  },
});

Alerting

Set up alerts for critical issues:

await client.monitoring.createAlert({
  name: 'high-error-rate',
  condition: 'error_rate > 0.05',
  notification: {
    type: 'email',
    recipients: ['ops@example.com'],
  },
});

Error Handling

Retry Logic

Implement exponential backoff:

async function executeWithRetry(
  fn: () => Promise,
  maxRetries = 3
) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      
      const delay = Math.pow(2, i) * 1000;
      await sleep(delay);
    }
  }
}

Circuit Breaker

Prevent cascading failures:

import CircuitBreaker from 'opossum';

const options = {
  timeout: 3000,
  errorThresholdPercentage: 50,
  resetTimeout: 30000,
};

const breaker = new CircuitBreaker(
  async () => client.tools.execute({ tool: 'api-call' }),
  options
);

Cost Optimization

Request Batching

Reduce API calls by batching requests:

// Instead of multiple single requests
for (const id of ids) {
  await client.databases.query({ sql: SELECT * FROM users WHERE id = ${id} });
}

// Use a single batched request
await client.databases.query({ 
  sql: SELECT * FROM users WHERE id IN (${ids.join(',')})
});

Compression

Enable compression for large payloads:

const client = new MCPClient({
  apiKey: process.env.MCP_API_KEY,
  compression: {
    enabled: true,
    algorithm: 'gzip',
  },
});

Best Practices

1. Use caching for frequently accessed data

2. Implement health checks for all services

3. Monitor error rates and set up alerts

4. Use connection pooling for databases

5. Enable request compression for large payloads

6. Implement circuit breakers to prevent cascading failures

7. Use batch operations when possible

8. Set appropriate timeouts for all operations

Next Steps

Troubleshooting Guide

Authentication & Security