MCP Performance Optimization: Production AI Traffic Guide

Critical Transport Selection

STDIO Transport: DO NOT USE

• Failure Mode: Most requests fail outright under load
• Performance: 1-2 RPS maximum, 20+ second response times
• Production Impact: Complete service failure, discovered during Tuesday production outage
• Root Cause: Each connection requires dedicated container attachment, overwhelmed by AI burst traffic
• Documentation Gap: Official docs do not warn about production limitations

SSE Transport: DEPRECATED

• Status: Works better than STDIO but deprecated technology
• Capacity: 20-30 concurrent users, 20-40 RPS
• Risk: Building on dead-end technology

HTTP Transport: PRODUCTION VIABLE

• Requirement: Session strategy determines success/failure
• Shared Sessions: 1000+ users, good performance
• Unique Sessions: 40-60 users maximum, 25-35 RPS, terrible performance

Production Scaling Thresholds

Transport	Max Users	RPS Capacity	Success Rate	Production Viability
STDIO	10-20	1-2	Poor	Never use
SSE	20-30	20-40	Decent	Deprecated
HTTP (Unique)	40-60	25-35	Acceptable	Limited scale
HTTP (Shared)	1000+	High	Good	Only viable option

AI Traffic Failure Patterns

Burst Request Characteristics

• Single AI conversation generates dozens of parallel requests
• AI agents retry without backoff mechanisms
• Requests cluster in unpredictable bursts vs. steady web traffic

Database Connection Exhaustion

• Default PostgreSQL: ~100 max connections
• AI Agent Behavior: Attempts to open far more connections simultaneously
• Failure Message: FATAL: sorry, too many clients already
• Impact: Complete service crash

Memory Management Critical Points

Large Response Handling

• Problem: AI queries return 30-50MB JSON payloads
• Failure Mode: Node.js crashes during serialization
• Production Impact: Hours-long outages from single large queries
• Solution: Stream responses in chunks, 10MB response limit

Garbage Collection Under Load

• Issue: Large objects persist longer than expected
• Symptom: GC pauses increase progressively
• Mitigation: Force GC between large operations

Session Pooling Implementation

Dynamic Scaling Strategy

• Base Pool: Start with 10 sessions
• Scaling Logic: Monitor utilization, scale when hitting limits
• Maximum: Based on backend capacity (PostgreSQL ~200-300 connections)

Session Affinity Requirements

• Need: AI conversations require consistent sessions for context
• Implementation: Route conversation turns to same session
• Cleanup: Remove old conversations to prevent memory leaks

Circuit Breaker Configuration

• Trigger: 5 failures in 30 seconds
• Purpose: Fail fast when backend overwhelmed
• Behavior: Don't queue additional requests during failures

Kubernetes Production Configuration

Resource Limits Reality

• Memory: 512MB insufficient for large JSON responses (Node 18 OOM)
• CPU: Limits significantly affect JSON parsing performance
• Network: Service mesh adds latency that compounds under burst traffic

Deployment Considerations

• Transport: Use HTTP only (STDIO doesn't work in K8s)
• Scaling: Base on session pool utilization, not CPU/memory
• Mesh: Consider bypassing for internal MCP communication

Monitoring Requirements for AI Workloads

Critical Metrics (Standard Web Metrics Insufficient)

• Session pool utilization percentage
• Response payload size distribution
• Burst request rate patterns
• Memory pressure during large response processing
• Error rates during traffic spikes

Alert Thresholds

• Session pool >80% utilization
• Memory climbing during response processing
• GC pause times increasing

Caching Strategy for AI Queries

Traditional Caching Limitations

• AI agents ask identical questions with different phrasing
• "Show customer data" vs "Display customer information" = cache misses
• Key-value caching ineffective for semantic similarity

Implementation Options

• Semantic Caching: More effective but adds complexity
• Simple Caching: Longer TTLs may provide sufficient benefit
• Recommendation: Start simple, evaluate semantic caching if needed

Common Production Failures

Database Configuration

• Problem: PostgreSQL default settings (100 connections)
• Solution: Dynamic connection pooling, not static allocation

Response Size Management

• Problem: Attempting to serialize massive datasets in memory
• Solution: Streaming responses, hard size limits

Session Strategy

• Problem: Unique sessions per request
• Solution: Shared session pools with affinity routing

Monitoring Blind Spots

• Problem: Using web traffic metrics for AI workloads
• Solution: AI-specific metrics (session utilization, response sizes)

Implementation Sequence

1. Transport Selection: Use HTTP with shared sessions
2. Connection Pooling: Implement dynamic scaling (start with 10, scale based on utilization)
3. Response Streaming: Implement for responses >10MB
4. Circuit Breakers: Add for external dependencies
5. Monitoring: Implement AI-specific metrics
6. Resource Limits: Size based on actual AI traffic patterns

Breaking Points and Limits

Memory Exhaustion: 30-50MB responses crash Node.js serialization
Connection Limits: PostgreSQL defaults fail at AI traffic levels
Transport Failure: STDIO completely unusable in production
Session Performance: 10x performance difference between shared vs unique sessions
Scaling Wall: Traditional web optimization hits wall around 200 AI users

Production Readiness Checklist

• HTTP transport with shared session pooling
• Dynamic connection pool scaling
• Response streaming for large payloads
• Circuit breakers for external dependencies
• AI-specific monitoring metrics
• Resource limits based on AI traffic patterns
• Burst traffic testing (not steady load testing)