Mnemosyne
Provides deep visibility into Kubernetes clusters, enabling natural language queries about pod lifecycles, service topology, application context, cluster health, and operational history.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MnemosyneWhat pods are failing in the default namespace?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Mnemosyne
Memory and knowledge for your Kubernetes clusters
Mnemosyne is a Model Context Protocol (MCP) server that gives AI assistants deep visibility into Kubernetes clusters. Named after the Greek goddess of memory, Mnemosyne remembers and recalls everything about your cluster state, making Kubernetes operations accessible through natural language.
What Mnemosyne Remembers
Pod Lifecycles: Track pod status, restarts, and resource usage across namespaces
Service Topology: Understand service relationships and network connectivity
Application Context: Extract service descriptions and documentation from running pods
Cluster Health: Monitor node status and resource allocation
Operational History: Access logs and events with intelligent filtering
Related MCP server: Kubectl MCP Tool
Key Features
Natural Language Interface: Ask questions like "What pods are failing?" or "Show me the logs for the auth service"
Intelligent Service Discovery: Automatically finds and describes services using embedded documentation
Multi-Protocol Support: WebSocket and HTTP endpoints for flexible integration
Production Hardened: RBAC, health checks, structured logging, and security best practices
Zero-Config Deployment: Works out of the box with sensible defaults
Quick Start
1. Deploy to Kubernetes
# Quick deployment for development
./scripts/quick-deploy.sh
# Production deployment
IMAGE_TAG=v1.0.0 NAMESPACE=production ./scripts/build-and-deploy.sh2. Connect Your AI Assistant
Add Mnemosyne to your AI assistant's MCP configuration:
{
"mcpServers": {
"mnemosyne": {
"command": "websocket",
"args": ["ws://localhost:8080/ws"],
"disabled": false,
"autoApprove": ["list_pods", "get_pod_logs", "describe_service"]
}
}
}3. Start Asking Questions
Now you can ask your AI assistant natural language questions about your cluster:
"What pods are running in the production namespace?"
"Show me the logs for the failing authentication service"
"What's the resource usage of the database pods?"
"Describe the user-service and its dependencies"
"Which nodes are under pressure?"
Mnemosyne translates these questions into the appropriate Kubernetes operations and returns human-readable results.
Mnemosyne's Capabilities
Capability | What It Remembers | MCP Tool |
Pod Intelligence | Status, health, containers, restarts |
|
Log Memory | Application logs with context and filtering |
|
Resource Awareness | CPU, memory usage and limits |
|
Service Knowledge | Service topology and embedded documentation |
|
File Access | Configuration files and application data |
|
Cluster Overview | Node status and cluster health |
|
Special Feature: Service Documentation Discovery
Mnemosyne automatically looks for /mnemosyne/serviceDescription.txt files in your pods to provide rich context about your services. This makes troubleshooting and understanding service relationships much more intuitive.
How Mnemosyne Works
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ AI Assistant │───▶│ Mnemosyne │───▶│ Kubernetes │
│ (Natural │ │ (Memory & │ │ Cluster │
│ Language) │ │ Translation) │ │ (Reality) │
└─────────────────┘ └──────────────────┘ └─────────────────┘Mnemosyne acts as the memory layer between AI assistants and Kubernetes:
Memory Layer: Remembers cluster state, relationships, and context
Translation Layer: Converts natural language to kubectl operations
Intelligence Layer: Enriches raw Kubernetes data with meaningful insights
Protocol Layer: Speaks MCP to AI assistants and kubectl to clusters
Development
Local Development
# Setup development environment
./scripts/local-dev.sh setup
# Run locally
./scripts/local-dev.sh run
# Run tests
./scripts/local-dev.sh test
# Build and run in Docker
./scripts/local-dev.sh build
./scripts/local-dev.sh dockerConfiguration
Environment variables:
Variable | Default | Description |
|
| Enable debug logging |
|
| Server bind address |
|
| Server port |
| (default) | Kubectl context |
|
| Allowed CORS origins |
|
| Max concurrent connections |
|
| Request timeout (seconds) |
Deployment
Prerequisites
Kubernetes 1.19+
kubectl configured
Docker (for building images)
Appropriate RBAC permissions
Deployment Scripts
scripts/build-and-deploy.sh- Full build and deployscripts/quick-deploy.sh- Quick development deploymentscripts/local-dev.sh- Local development tools
Kubernetes Resources
The deployment includes:
Deployment: Server pods with resource limits
Service: Internal cluster access
Ingress: External access configuration
ServiceAccount: RBAC permissions
ConfigMap: Configuration management
Security
RBAC Permissions
Minimal required permissions:
pods: get, list, watchpods/log: getpods/exec: createservices: get, listnodes: get, list
Container Security
Runs as non-root user
Minimal base image
Health checks enabled
Resource limits configured
Network Security
CORS configuration
TLS support ready
Network policy compatible
Monitoring
Health Endpoints
/health- Liveness probe/ready- Readiness probe
Logging
Structured logging with configurable levels:
Request/response logging
kubectl command logging (debug)
Error tracking with correlation IDs
Metrics
Monitor:
Response times
Error rates
Connection counts
Resource usage
Documentation
Deployment Guide - Comprehensive deployment instructions
Troubleshooting Guide - Common issues and solutions
Configuration Examples - Environment-specific configurations
Troubleshooting
Common Issues
Pod not starting: Check image availability and resource limits
kubectl errors: Verify RBAC permissions and kubeconfig
Connection issues: Check service/ingress configuration and CORS
Performance: Monitor resource usage and optimize queries
Debug Mode
Enable debug logging:
kubectl set env deployment/mnemosyne DEBUG=true -n mcp-serverGetting Help
See TROUBLESHOOTING.md for detailed guidance.
Why Mnemosyne?
In Greek mythology, Mnemosyne was the goddess of memory and the mother of the nine Muses. She gave mortals the ability to remember and learn from experience.
Similarly, this Mnemosyne gives AI assistants the ability to remember and understand your Kubernetes clusters, transforming raw operational data into actionable insights.
Contributing
Mnemosyne welcomes contributions! Whether you're adding new capabilities, improving documentation, or fixing bugs:
Fork the repository
Create a feature branch (
git checkout -b feature/amazing-memory)Make your changes with tests
Submit a pull request
License
MIT License - See LICENSE file for details
Support
Mnemosyne remembers everything, but sometimes you need help too:
📖 Troubleshooting Guide - Common issues and solutions
🚀 Deployment Guide - Production deployment best practices
💬 Open an issue with your cluster context and we'll help debug
"Memory is the treasury and guardian of all things." - Cicero
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