MCP Cluster Demo

AI agentic system for performing academic research using mcp-amqp-transport framework

Architecture

Interceptor Pipeline:

1. Analytics → Logging the messages to Opensearch for analytics
2. Safety → Rate limiting + content filtering

MCP Servers:

• Firecrawl (web scraping)
• Arxiv (academic papers)

Quick Start

# Start Kubernetes
minikube start
eval $(minikube docker-env)

# Build and deploy MCP servers
cd mcp_cluster
docker build -f Dockerfile.amqp-transport -t mcp-amqp-transport:latest .
kubectl apply -f k8s/

# Build and deploy interceptors
cd ../interceptors_cluster
docker build -f Dockerfile.monitor -t monitor-interceptor:latest .
docker build -f Dockerfile.safety -t safety-interceptor:latest .
docker build -f Dockerfile.opensearch-logger -t opensearch-logger:latest .
kubectl apply -f k8s_storage/
kubectl apply -f k8s/

# Run agent
cd ../agent
uv sync
uv run agent.py

Configuration

Update ConfigMaps with RabbitMQ credentials:

• mcp_cluster/k8s/amqp-config.yaml
• interceptors_cluster/k8s/*-config.yaml

Monitoring

kubectl get pods
kubectl logs -f deployment/safety-interceptor
kubectl port-forward service/opensearch-dashboards 5601:5601

Features

• Fault Tolerance: Message persistence prevents data loss
• Horizontal Scaling: Add servers by binding queues to exchanges
• Security: 20 calls/client limit, 600 word response limit
• Observability: CloudWatch + OpenSearch logging
• Analytics: Token counting and message indexing

Cleanup

kubectl delete -f interceptors_cluster/k8s/
kubectl delete -f mcp_cluster/k8s/
minikube stop