Multi-Agent System with MCP Architecture - AI-Optimized Technical Reference

Critical System Overview

Technology Stack: MCP (Model Context Protocol) with JSON-RPC 2.0 communication
Architecture Pattern: Coordinator-worker pattern (single point of failure)
Language: Python 3.8+ (3.9 recommended due to asyncio issues in 3.8)
Communication Protocol: JSON-RPC 2.0 over HTTP

Resource Requirements

Hardware Specifications

• Minimum RAM: 16GB (documentation claims 8GB but fails with 4 agents)
• Network: Fast internet required (500MB+ dependency downloads)
• Storage: Monitor Docker disk usage (containers consume significant space)

Time Investment

• Setup Time: 4-6 hours minimum (tutorials underestimate by 50-75%)
• Debugging Allocation: 70% of development time spent on connection issues
• Learning Curve: Expect weeks of debugging distributed systems failures

Core Dependencies and Installation Issues

Required Packages

pip install fastmcp httpx asyncio aiofiles
pip install openai anthropic pydantic jsonschema

Common Installation Failures

Issue	Solution	Frequency
fastmcp fails on Windows	Use WSL2 or abandon Windows	High
httpx timeout errors	Network issues, retry installation	Medium
Import errors with asyncio	Upgrade from Python 3.7	Low

Architecture Components and Failure Modes

MCP Component Types (Source of Confusion)

1. MCP Hosts - Where LLM runs (Claude Desktop, custom app)
2. MCP Clients - Translation layer between host and servers
3. MCP Servers - Actual agents performing work

Critical Note: Each agent is both client AND server (dual role causes debugging complexity)

Coordinator Agent - Single Point of Failure

Function: Task decomposition, agent assignment, result aggregation
Memory Leaks: Python asyncio loops leak memory in long-running services
Timeout Handling: Default 60 seconds (increase for production)

Common Coordinator Failures

• Agents disappear when containers restart
• Task decomposition fails during LLM bad performance days
• Network timeouts kill entire workflow
• Result aggregation crashes on malformed JSON

Configuration That Works

# Realistic timeout settings
timeout_seconds: int = 60
health_check_interval: int = 60  # seconds
max_concurrent_tasks: int = 10   # start low

Worker Agent Specifications

Research Agent - Web Scraper

Capabilities: Web search, content extraction
Rate Limits: Google free tier = 100 searches/day
Memory Pattern: Minimal memory usage
Failure Threshold: Breaks after ~20 requests without rate limiting

Critical Settings:

• Minimum 1 second between requests
• Cache results for 24 hours
• Use multiple search APIs with fallback
• Implement exponential backoff

Analysis Agent - Data Processor

Memory Limit: Crashes on datasets > 100MB
Safe Row Limit: 10,000 rows maximum
Technology Constraint: Pandas not suitable for production workloads
Memory Growth: Exponential with data size

Breaking Points:

• 50MB+ datasets cause OOM crashes
• Complex nested data structures break DataFrame conversion
• Correlation analysis fails randomly on large datasets

Reporter Agent - Markdown Generator

Function: Data to markdown conversion
Reliability: Highest (simplest component)
Memory Usage: Minimal
Failure Mode: Crashes on complex nested data structures

Error Codes and Debugging

JSON-RPC Error Codes (Memorize These)

Code	Meaning	Common Cause
-32601	Method not found	Tool not registered or agent not loaded
-32602	Invalid parameters	Schema validation failure
-32603	Internal error	Agent crashed or network issue

Network Debugging Commands

# Check container connectivity
docker exec -it container ping other_container

# Monitor container resource usage  
docker stats

# View container logs
docker logs container_name --follow --tail 100

# Check health endpoints
curl -f http://localhost:8000/health

Production Configuration

Docker Resource Limits

services:
  analyzer:
    mem_limit: 2g  # Prevent memory bombs
    restart: unless-stopped
    healthcheck:
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 30s

Environment Variables

LOG_LEVEL=INFO
MAX_CONCURRENT_TASKS=10  # Start low, increase carefully
REQUEST_TIMEOUT=30       # 30 seconds sufficient
HEALTH_CHECK_INTERVAL=60 # Check agents every minute

Performance Characteristics

Load Testing Results

• Breaking Point: System fails at 20+ concurrent requests
• Success Rate: <80% at failure threshold
• Response Time: Increases linearly with concurrent load
• Memory Usage: Grows with agent count and data size

Scaling Limitations

• Agent Limit: Don't exceed 10 agents (architecture doesn't scale)
• Data Processing: 10K rows maximum per analysis
• Concurrent Tasks: 10 maximum for stability

Critical Warnings and Hidden Costs

What Official Documentation Doesn't Tell You

• Default settings fail in production
• Memory leaks are inevitable with long-running Python services
• Network issues cause cascade failures
• LLM decomposition fails randomly
• Docker containers develop networking problems over time

Operational Costs

• Human Time: 70% debugging, 30% feature development
• Infrastructure: 16GB RAM minimum, fast network required
• API Costs: Rate limiting forces paid API subscriptions
• Monitoring: Essential for production (Prometheus + Grafana recommended)

Breaking Points and Failure Modes

• Container Restarts: Agents disappear from coordinator registry
• Memory Exhaustion: Analysis agent OOMs on realistic datasets
• Rate Limiting: Research agent blocked after minimal usage
• Network Partitions: Coordinator loses track of healthy agents
• Cascade Failures: One agent failure can kill entire workflow

Migration and Scaling Considerations

When to Abandon This Architecture

• More than 10 agents required
• Processing datasets > 100MB
• High availability requirements
• Consistent low-latency needs

Alternative Technologies

• Message Queues: Redis, RabbitMQ for >10 agents
• Microservices: Proper frameworks for scale
• Databases: PostgreSQL instead of pandas for data processing
• Languages: Go for better concurrent performance

Monitoring and Alerting Requirements

Essential Metrics

• Agent health status (binary: healthy/unhealthy)
• Request success rate (target: >95%)
• Average response time (target: <30 seconds)
• Memory usage per container (alert at 80%)
• Error rate by agent type

Alert Conditions

• Any agent unhealthy > 2 minutes
• Success rate < 80% over 5 minutes
• Memory usage > 80% of container limit
• Response time > 60 seconds
• Error rate > 10% over 10 minutes

Testing Strategy

Local Testing Limitations

• Perfect local performance creates false confidence
• Network latency absent in local environment
• Resource constraints not realistic
• Real API failures not simulated

Integration Testing Requirements

• Mock all external APIs (prevent rate limiting)
• Test with realistic network delays
• Simulate container restarts
• Test concurrent load scenarios
• Validate error handling paths

Implementation Decision Tree

Choose MCP When:

• Building demo or prototype system
• Need tool sharing between agents
• Have <10 agents total
• Processing small datasets (<10K rows)
• Can tolerate single point of failure

Avoid MCP When:

• Require high availability
• Need to scale beyond 10 agents
• Processing large datasets (>100MB)
• Cannot tolerate coordinator failures
• Need consistent low latency

Troubleshooting Quick Reference

Agent Registration Issues

1. Check agent is running: curl http://agent:port/health
2. Verify coordinator can reach agent network
3. Check Docker networking configuration
4. Validate agent tool registration completion

Task Execution Failures

1. Check coordinator logs for assignment errors
2. Verify agent health status in registry
3. Test individual agent endpoints directly
4. Check for memory or timeout issues

Performance Degradation

1. Monitor memory usage across all containers
2. Check for network connectivity issues
3. Verify no agents stuck in infinite loops
4. Review concurrent task limits

This architecture works for demonstrations and small-scale systems but requires significant operational overhead and has fundamental scaling limitations. Plan migration strategy before reaching operational limits.