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K3s
БесплатноНе проверенModel Context Protocol server for K3s cluster management - kubectl operations for Claude
Описание
Model Context Protocol server for K3s cluster management - kubectl operations for Claude
README
A Model Context Protocol (MCP) server for managing Kubernetes (K3s) clusters. This server enables AI assistants like Claude to manage K3s clusters through natural language, and serves as the foundation for Cortex Platform - an AI-native infrastructure orchestration system.
Cortex Platform: AI-Native Infrastructure on K3s
This project is part of the Cortex Platform, a production system that demonstrates advanced K3s usage patterns:
- 7-Layer Serverless Fabric with KEDA auto-scaling (0→1 pods on demand)
- AI-powered query routing with multi-tier classification
- Self-healing infrastructure through MCP-based automation
- Vector memory (Qdrant) for learning from operational patterns
- Dynamic worker pools managed by AI agents
Architecture Overview
┌─────────────────────────────────────────────────────────────────────────────┐
│ CORTEX PLATFORM │
│ AI-Native K3s Infrastructure │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ K3s CLUSTER (7 nodes) │ │
│ │ Talos Linux + etcd HA │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────────────────────────┐ │ │
│ │ │ UNIFI LAYER FABRIC │ │ │
│ │ │ Serverless AI Network Operations │ │ │
│ │ │ │ │ │
│ │ │ USER QUERY │ │ │
│ │ │ │ │ │ │
│ │ │ ▼ │ │ │
│ │ │ ┌───────────────────┐ ┌─────────────────┐ │ │ │
│ │ │ │ CORTEX ACTIVATOR │───▶│ CORTEX QDRANT │ │ │ │
│ │ │ │ (Always On) │ │ (Always On) │ │ │ │
│ │ │ │ Query Router │ │ Vector Memory │ │ │ │
│ │ │ │ 128MB, 2 pods │ │ 512MB, 5Gi PVC │ │ │ │
│ │ │ └─────────┬─────────┘ └─────────────────┘ │ │ │
│ │ │ │ │ │ │
│ │ │ │ 4-Tier Routing Cascade │ │ │
│ │ │ │ 2. Similarity Search (<50ms) │ │ │
│ │ │ │ 3. Lightweight Classifier (~5s cold) │ │ │
│ │ │ │ 4. Full SLM Reasoning (~12s cold) │ │ │
│ │ │ ▼ │ │ │
│ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │
│ │ │ │ REASONING LAYERS (Scale 0→1) │ │ │ │
│ │ │ │ │ │ │ │
│ │ │ │ ┌──────────────────┐ ┌──────────────────────┐ │ │ │ │
│ │ │ │ │ reasoning- │ │ reasoning-slm │ │ │ │ │
│ │ │ │ │ classifier │ │ │ │ │ │ │
│ │ │ │ │ Qwen2 0.5B │ │ Phi-3 3.8B │ │ │ │ │
│ │ │ │ │ ~5s cold start │ │ ~12s cold start │ │ │ │ │
│ │ │ │ │ 400MB warm │ │ 2.5GB warm │ │ │ │ │
│ │ │ │ └──────────────────┘ └──────────────────────┘ │ │ │ │
│ │ │ └─────────────────────────────────────────────────────┘ │ │ │
│ │ │ │ │ │ │
│ │ │ ▼ │ │ │
│ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │
│ │ │ │ EXECUTION LAYERS (Scale 0→1) │ │ │ │
│ │ │ │ │ │ │ │
│ │ │ │ ┌──────────────────┐ ┌──────────────────────┐ │ │ │ │
│ │ │ │ │ execution- │ │ execution- │ │ │ │ │
│ │ │ │ │ execution- │ │ execution- │ │ │ │ │
│ │ │ │ │ execution- │ │ execution- │ │ │ │ │
│ │ │ │ │ unifi-api │ │ unifi-ssh │ │ │ │ │
│ │ │ │ │ unifi-api │ │ unifi-ssh │ │ │ │ │
│ │ │ │ │ Primary │ │ Failover │ │ │ │ │
│ │ │ │ │ ~3s cold start │ │ ~3s cold start │ │ │ │ │
│ │ │ │ └──────────────────┘ └──────────────────────┘ │ │ │ │
│ │ │ └─────────────────────────────────────────────────────┘ │ │ │
│ │ │ │ │ │ │
│ │ │ ▼ │ │ │
│ │ │ ┌───────────────────┐ │ │ │
│ │ │ │ CORTEX TELEMETRY │ │ │ │
│ │ │ │ Metrics + Learning│ │ │ │
│ │ │ │ Scale 0→1 │ │ │ │
│ │ │ └───────────────────┘ │ │ │
│ │ └─────────────────────────────────────────────────────────────┘ │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────────────────────────┐ │ │
│ │ │ MCP SERVERS │ │ │
│ │ │ │ │ │
│ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │
│ │ │ │ k3s-mcp │ │ talos-mcp │ │ proxmox-mcp │ │ │ │
│ │ │ │ (this repo) │ │ │ │ │ │ │ │
│ │ │ │ Cluster ops │ │ Node mgmt │ │ VM lifecycle │ │ │ │
│ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │
│ │ └─────────────────────────────────────────────────────────────┘ │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────────────────────────┐ │ │
│ │ │ DYNAMIC WORKER POOLS │ │ │
│ │ │ (Managed by AI Resource Manager) │ │ │
│ │ │ │ │ │
│ │ │ Permanent: 3-10 nodes (always running) │ │ │
│ │ │ Burst: 0-20 nodes (TTL-based cleanup) │ │ │
│ │ │ Spot: 0-15 nodes (70% cost savings) │ │ │
│ │ │ GPU: 0-5 nodes (special hardware taints) │ │ │
│ │ └─────────────────────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
│ Memory Profile: │
│ • Idle: 640MB (Activator + Qdrant only) │
│ • Simple query: 1GB (+ execution layer) │
│ • Complex: 4GB (+ SLM reasoning) │
│ • Savings: 85%+ vs always-on architecture │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
The 7 Layers
| Layer | Component | Purpose | Memory | Cold Start | Scale |
|---|---|---|---|---|---|
| 1 | cortex-activator | Query routing & orchestration | 128MB | Always on | 2 replicas |
| 2 | cortex-qdrant | Vector memory & RAG | 512MB | Always on | 1 replica |
| 3 | reasoning-classifier | Fast intent classification | 400MB | ~5s | 0→1 |
| 4 | reasoning-slm | Full reasoning (Phi-3) | 2.5GB | ~12s | 0→1 |
| 5 | execution-unifi-api | Primary API operations | 200MB | ~3s | 0→2 |
| 6 | execution-unifi-ssh | Failover & diagnostics | 100MB | ~3s | 0→1 |
| 7 | cortex-telemetry | Metrics & learning pipeline | 128MB | ~2s | 0→1 |
Cortex Activator: Intelligent Query Router
The Cortex Activator is the brain of the system - a lightweight service that routes queries through a 4-tier cascade:
Query: "Block the client with MAC aa:bb:cc:dd:ee:ff"
│
▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 1: Keyword Pattern Match (<10ms) │
│ │
│ Pattern: "(block|unblock).*client" → MATCH │
│ Confidence: 95% │
│ Action: Route directly to execution-unifi-api │
└─────────────────────────────────────────────────────────────┘
│
│ (If no match, continue to Tier 2)
▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 2: Qdrant Similarity Search (<50ms) │
│ │
│ Query embedding → Search past successful routes │
│ If similar query succeeded before → Reuse routing │
│ Learning: Skip expensive LLM classification │
└─────────────────────────────────────────────────────────────┘
│
│ (If similarity < 92%, continue to Tier 3)
▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 3: Lightweight Classifier (~5s cold start) │
│ │
│ Model: Qwen2-0.5B (quantized) │
│ Use: Ambiguous queries needing quick classification │
│ KEDA: Scales from 0→1 on demand │
└─────────────────────────────────────────────────────────────┘
│
│ (If complex investigation needed, continue to Tier 4)
▼
┌─────────────────────────────────────────────────────────────┐
│ TIER 4: Full SLM Reasoning (~12s cold start) │
│ │
│ Model: Phi-3-mini-4k-instruct (3.8B, quantized) │
│ Use: Multi-step reasoning, complex troubleshooting │
│ KEDA: Scales from 0→1 on demand │
└─────────────────────────────────────────────────────────────┘
KEDA Serverless Scaling
Layers scale from 0 to 1 based on Prometheus metrics:
# reasoning-slm KEDA configuration
keda:
minReplicaCount: 0 # Scale to zero when idle
maxReplicaCount: 1
cooldownPeriod: 300 # Scale down after 5 min idle
trigger:
type: prometheus
query: sum(cortex_activator_pending_requests{layer="reasoning-slm"})
threshold: "1" # Wake if ANY pending request
Activation flow:
- Query arrives → Activator increments
pending_requestsgauge - KEDA detects metric > threshold → Scales deployment 0→1
- Pod starts → Health probe passes → Pod ready
- Request processed → Response sent
- 5 minutes idle → Cooldown triggers → Scales back to 0
Adaptive Intelligence (Phase 4)
Query complexity scoring (0-100) determines execution mode:
| Complexity | Score | Mode | Resources |
|---|---|---|---|
| SIMPLE | 0-25 | Direct execution | Activator only |
| MODERATE | 26-50 | Basic classification | + Classifier |
| COMPLEX | 51-75 | Full reasoning | + SLM |
| EXPERT | 76-100 | Escalation | Human review |
Auto-escalation triggers:
- Low confidence (<50%) → Escalate mode
- Previous similar queries failed → Escalate
- Timeout (>30s agent, >60s hybrid) → Escalate
K3s MCP Server Features
This MCP server provides the foundation for AI-driven cluster management:
Pod Management
- List pods across namespaces with label selectors
- Get pod logs with tail and container selection
- Execute commands in pods
- Restart pods (delete and recreate)
Deployment Management
- List and describe deployments
- Scale deployments up or down
- Get deployment status and replica counts
Service Management
- List services and endpoints
- View service ports and selectors
- Check service types (ClusterIP, NodePort, LoadBalancer)
Node Management
- List all cluster nodes
- Get node status and resources
- View node capacity and allocatable resources
- Check node conditions (Ready, MemoryPressure, etc.)
Resource Management
- Apply YAML manifests
- Delete resources (pods, deployments, services)
- List namespaces
- Get cluster information
Quick Start
# 1. Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh
# 2. Clone this repository
git clone https://github.com/ry-ops/k3s-mcp-server.git
cd k3s-mcp-server
# 3. Run setup script
chmod +x setup.sh
./setup.sh
# 4. Set environment variables
export KUBECONFIG="$HOME/.kube/config"
# 5. Test the server
uv run k3s-mcp-server
# 6. Configure Claude Desktop and restart
See QUICKSTART.md for detailed instructions.
Installation
Prerequisites
- Python 3.10 or higher
uvpackage manager- K3s cluster with kubeconfig access
- Kubeconfig file
Setup
# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# Clone this repository
git clone https://github.com/ry-ops/k3s-mcp-server.git
cd k3s-mcp-server
# Run setup script (creates structure and installs dependencies)
./setup.sh
# Or manually:
uv sync
Configuration
Environment Variables
| Variable | Description | Default |
|---|---|---|
KUBECONFIG |
Path to kubeconfig file | ~/.kube/config |
K3S_DEFAULT_NAMESPACE |
Default namespace | default |
K3S_DEBUG |
Enable debug logging | false |
Claude Desktop Configuration
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"k3s": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/k3s-mcp-server",
"run",
"k3s-mcp-server"
],
"env": {
"KUBECONFIG": "/path/to/.kube/config"
}
}
}
}
Available Tools
Pod Tools
| Tool | Description | Parameters |
|---|---|---|
get_pods |
List pods in namespace or cluster-wide | namespace, labels |
get_logs |
Get pod logs | pod_name, namespace, container, tail_lines |
restart_pod |
Restart a pod by deleting it | name, namespace |
execute_command |
Execute command in pod | pod_name, namespace, command, container |
Deployment Tools
| Tool | Description | Parameters |
|---|---|---|
get_deployments |
List all deployments | namespace |
get_deployment |
Get specific deployment details | name, namespace |
scale_deployment |
Scale deployment replicas | name, namespace, replicas |
Cluster Tools
| Tool | Description | Parameters |
|---|---|---|
get_nodes |
List all nodes with resources | - |
get_services |
List all services | namespace |
get_cluster_info |
Get cluster version and summary | - |
get_namespaces |
List all namespaces | - |
apply_manifest |
Apply YAML manifest | manifest_yaml, namespace |
delete_resource |
Delete a resource | kind, name, namespace |
Example Usage
Once configured, ask Claude to interact with your K3s cluster:
"List all pods in the cortex-system namespace"
"What's the status of the cortex-activator deployment?"
"Scale reasoning-slm to 1 replica"
"Show me logs from the cortex-qdrant pod"
"Which nodes are in my cluster and what's their capacity?"
"Apply this deployment manifest: [YAML content]"
Project Structure
k3s-mcp-server/
├── src/
│ └── k3s_mcp_server/
│ ├── __init__.py # Package initialization
│ └── server.py # Main server implementation
├── docs/
│ ├── ARCHITECTURE.md # Detailed architecture docs
│ └── CORTEX_INTEGRATION.md # Cortex Platform integration
├── pyproject.toml # Project configuration
├── uv.lock # Locked dependencies
├── setup.sh # Automated setup script
├── README.md # This file
└── QUICKSTART.md # Quick setup guide
K3s Features Used
This project demonstrates production usage of K3s features:
| Feature | Usage |
|---|---|
| Namespaces | Multi-tenant isolation (cortex-system, cortex-mcp, cortex-unifi) |
| Deployments | All workloads with rolling updates |
| KEDA | Serverless 0→1 scaling for reasoning/execution layers |
| Helm | Package management for all components |
| RBAC | Fine-grained service account permissions |
| PVCs | Persistent storage for Qdrant vector database |
| ConfigMaps/Secrets | Configuration management |
| Health Probes | Liveness, readiness, startup probes |
| Resource Limits | CPU/memory requests and limits |
| Pod Anti-Affinity | HA distribution across nodes |
| ArgoCD | GitOps continuous deployment |
Security Considerations
- Kubeconfig Security: Keep your kubeconfig file secure (chmod 600)
- RBAC: Ensure appropriate permissions for the kubeconfig user
- Network Access: Secure network connectivity to K3s API server
- Audit: K8s API server logs all actions for auditing
Troubleshooting
Connection Errors
- Verify
KUBECONFIGpath is correct and file exists - Check network connectivity to K3s server
- Test with:
kubectl --kubeconfig /path/to/config get nodes
Authentication Errors
- Verify kubeconfig contains valid credentials
- Check if certificates are valid and not expired
Tools Not Showing in Claude
- Verify absolute path in Claude config
- Check that config file is valid JSON
- Restart Claude Desktop completely
Debug Mode
export K3S_DEBUG=true
uv run k3s-mcp-server
Roadmap
- ConfigMap and Secret management
- PersistentVolume and PVC operations
- Ingress management
- Job and CronJob support
- StatefulSet operations
- HorizontalPodAutoscaler (HPA) configuration
- Helm chart deployment support
- KEDA ScaledObject management
Dependencies
- mcp (>=1.0.0): Model Context Protocol SDK
- kubernetes (>=29.0.0): Official Python client for Kubernetes
- pyyaml (>=6.0): YAML parser for manifest handling
Contributing
Contributions welcome! Areas for improvement:
- Additional resource types
- Better error handling
- Performance optimizations
- Documentation improvements
- Test coverage
License
MIT
Related Projects
- Model Context Protocol
- K3s - Lightweight Kubernetes
- KEDA - Kubernetes Event-driven Autoscaling
- Qdrant - Vector Database
- uv - Python Package Manager
Part of the Cortex Platform - AI-native infrastructure orchestration on K3s
Установка K3s
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/ry-ops/k3s-mcp-serverFAQ
K3s MCP бесплатный?
Да, K3s MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для K3s?
Нет, K3s работает без API-ключей и переменных окружения.
K3s — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить K3s в Claude Desktop, Claude Code или Cursor?
Открой K3s на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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