Deploy MCP Server

​

Request

​

Response

curl -X POST https://api.leanmcp.com/v1/deploy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "mcp_id": "mcp_abc123def456",
    "environment": "production",
    "config": {
      "domain": "my-mcp.example.com",
      "scaling": {
        "min_instances": 2,
        "max_instances": 20
      },
      "env_vars": {
        "API_KEY": "your-api-key",
        "DEBUG": "false"
      }
    }
  }'

curl -X POST https://api.leanmcp.com/v1/deploy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "mcp_id": "mcp_abc123def456",
    "environment": "staging"
  }'

{
  "success": true,
  "data": {
    "deployment_id": "deploy_xyz789abc",
    "status": "deployed",
    "url": "https://my-mcp.example.com",
    "deploy_time": 45.2,
    "health_check": "https://my-mcp.example.com/health"
  },
  "meta": {
    "request_id": "req_deploy_456", 
    "timestamp": "2023-12-01T12:00:00Z"
  }
}

​

Deployment Environments

​

Production

• High availability - Multiple instances across regions
• Auto-scaling - Scales based on demand
• Monitoring - Full observability and alerting
• Custom domains - Use your own domain
• SSL certificates - Automatic HTTPS

​

Staging

• Pre-production testing - Test before going live
• Shared resources - Lower cost than production
• Limited scaling - Fewer instances
• Temporary URLs - Auto-generated domains

​

Development

• Quick iterations - Fast deploy cycles
• Single instance - Minimal resources
• Debug mode - Enhanced logging
• Auto-cleanup - Removes old deployments

​

Deployment Status

GET https://api.leanmcp.com/v1/deploy/deploy_xyz789abc/status

• deploying: Deployment in progress
• deployed: Successfully deployed and running
• failed: Deployment failed
• stopped: Deployment manually stopped

​

Best Practices

​

Before Deploying

1. Test thoroughly - Use /v1/test endpoint
2. Check performance - Monitor response times
3. Validate tools - Ensure all tools work correctly
4. Review logs - Check for errors in development

​

Production Checklist

• All tools tested with AI
• Resource access permissions configured
• Environment variables set
• Custom domain configured (if needed)
• Monitoring alerts configured
• Backup strategy planned

​

Rollback Plan

1. Check deployment logs
2. Fix issues in development
3. Redeploy with fixes
4. Or rollback to previous version

​

Monitoring

• Health checks - Automatic uptime monitoring
• Performance metrics - Response times and throughput
• Error rates - Track failed tool calls
• Usage analytics - See how AI agents use your MCP