mcpField
M
Multi Cluster Kubernetes MCP Server

Multi Cluster Kubernetes MCP Server

Local
github.com/razvanmacoveiCloud & Infra15Updated 29w ago

About Multi Cluster Kubernetes MCP Server

Multi Cluster Kubernetes MCP Server exposes Kubernetes operations through MCP and discovers clusters across multiple kubeconfig files. It is for operators who need centralized resource management, monitoring, RBAC, storage, and networking tools, including cross-cluster comparisons.

Key features

  1. 01Automatic kubeconfig context discovery
  2. 02Cross-cluster resource and status comparisons
  3. 03Cluster resource management and diagnosis
  4. 04Monitoring, RBAC, storage, and networking tools
  5. 05Context caching to reduce repeated scans

Use cases

  1. 01Checking development, staging, and production clusters centrally
  2. 02Comparing resources and health across clusters
  3. 03Diagnosing applications and inspecting pod logs
  4. 04Scaling, rolling back, and maintaining nodes

MCP support

It exposes over 60 Kubernetes tools to MCP clients, covering cluster operations, resource management, monitoring, RBAC, storage, and networking.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"k8s_multicluster_mcp": {
"command": "npx",
"args": [
"-y",
"@smithery/cli"
]
}
}
}
Run commandnpx -y @smithery/cli

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@smithery/cli

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Multi Cluster Kubernetes MCP Server

smithery badge Python 3.11+ License: Apache 2.0

An MCP (Model Context Protocol) server for managing multiple Kubernetes clusters simultaneously. Provides 60+ tools covering cluster operations, resource management, monitoring, RBAC, storage, networking, and more -- all accessible through AI assistants like Claude Desktop.

Quick Start

Installing via Smithery
npx -y @smithery/cli install @razvanmacovei/k8s-multicluster-mcp --client claude
Manual Installation
git clone https://github.com/razvanmacovei/k8s-multicluster-mcp.git
cd k8s-multicluster-mcp
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python3 app.py
Docker
docker build -t k8s-mcp .
docker run -v ~/.kube:/root/.kube k8s-mcp

Configuration

Claude Desktop / MCP Client

Add to your MCP client configuration:

{
  "mcpServers": {
    "kubernetes": {
      "command": "python3",
      "args": ["/path/to/k8s-multicluster-mcp/app.py"],
      "env": {
        "KUBECONFIG_DIR": "/path/to/your/kubeconfigs"
      }
    }
  }
}
Environment Variables
VariableDefaultDescription
KUBECONFIG_DIR~/.kubeDirectory containing kubeconfig files. Each file can contain one or more contexts.

Place your kubeconfig files in the configured directory. The server discovers all contexts across all files automatically.

Prerequisites
  • Python 3.11 or higher
  • One or more kubeconfig files with valid cluster credentials
  • metrics-server installed in clusters for k8s_top_* and k8s_cluster_info tools

Multi-Cluster Management

This server is purpose-built for managing multiple Kubernetes clusters:

  • Automatic Discovery: Scans all kubeconfig files in KUBECONFIG_DIR and aggregates contexts
  • Partial Name Matching: Use prod instead of arn:aws:eks:us-east-1:123456:cluster/prod-cluster
  • Cross-Cluster Operations: Compare resources, health, and configurations across clusters
  • Context Caching: Efficient 30-second TTL cache avoids re-reading kubeconfig files on every call
  • Centralized Management: Manage dev, staging, and production from a single interface

Tools Reference (60+ tools)

Cluster & Context Management (6 tools)
ToolDescription
k8s_get_contextsList all available contexts across all kubeconfig files
k8s_get_namespacesList all namespaces in a cluster
k8s_create_nsCreate a new namespace with optional labels/annotations
k8s_delete_nsDelete a namespace (protects system namespaces)
k8s_get_nodesList all nodes with status, roles, capacity, and version
k8s_cluster_infoComprehensive cluster health summary (nodes, pods, deployments, warnings)
Resource Discovery & Inspection (7 tools)
ToolDescription
k8s_get_resourcesList resources of any kind (pods, deployments, services, ingress, etc.)
k8s_get_resourceGet the complete definition of a single resource
k8s_get_pod_logsGet pod logs with duration filtering and container selection
k8s_get_eventsList cluster events in a namespace, sorted by most recent
k8s_describeDetailed resource description similar to kubectl describe
k8s_apisList all available API groups and resources in the cluster
k8s_crdsList all Custom Resource Definitions with versions and scope
Metrics & Monitoring (3 tools)
ToolDescription
k8s_top_nodesDisplay node CPU/memory usage alongside capacity
k8s_top_podsDisplay pod/container CPU/memory usage
k8s_diagnose_applicationComprehensive app diagnostics with issue detection and recommendations
Rollout Management (6 tools)
ToolDescription
k8s_rollout_statusGet rollout status for deployment/statefulset/daemonset
k8s_rollout_historyGet revision history
k8s_rollout_undoRoll back to a previous revision
k8s_rollout_restartTrigger a rolling restart
k8s_rollout_pausePause an in-progress rollout
k8s_rollout_resumeResume a paused rollout
Scaling (3 tools)
ToolDescription
k8s_scale_resourceScale deployment/statefulset/replicaset to N replicas
k8s_autoscale_resourceConfigure Horizontal Pod Autoscaler (HPA)
k8s_update_resourcesUpdate CPU/memory requests and limits for a container
Resource CRUD (6 tools)
ToolDescription
k8s_create_resourceCreate a resource from YAML/JSON content
k8s_apply_resourceApply configuration (create or update, like kubectl apply)
k8s_delete_resourceDelete any resource type with optional force/grace period
k8s_patch_resourceUpdate specific fields with strategic merge patch
k8s_label_resourceAdd or update labels on a resource
k8s_annotate_resourceAdd or update annotations on a resource
Workload Management (3 tools)
ToolDescription
k8s_expose_resourceExpose a deployment/pod as a new Service
k8s_run_podCreate and run a pod with a specified image
k8s_set_resources_for_containerSet resource limits/requests for containers
Node Management (5 tools)
ToolDescription
k8s_cordon_nodeMark a node as unschedulable
k8s_uncordon_nodeMark a node as schedulable
k8s_drain_nodeDrain a node by evicting pods (for maintenance)
k8s_taint_nodeAdd taints to a node
k8s_untaint_nodeRemove taints from a node
Pod Operations (1 tool)
ToolDescription
k8s_pod_execExecute a command in a container (supports quoted arguments)
Secrets & ConfigMaps (6 tools)
ToolDescription
k8s_list_secretList secrets with metadata (values hidden for security)
k8s_get_secret_detailGet a secret; optionally decode values
k8s_create_secret_resourceCreate a new secret (auto base64-encodes values)
k8s_list_configmapList ConfigMaps with their key names
k8s_get_configmap_detailGet a ConfigMap with full data contents
k8s_create_configmap_resourceCreate a new ConfigMap
RBAC (5 tools)
ToolDescription
k8s_get_rolesList RBAC Roles with permission rules
k8s_get_clusterrolesList RBAC ClusterRoles
k8s_get_rolebindingsList RoleBindings (who has what role)
k8s_get_clusterrolebindingsList ClusterRoleBindings
k8s_get_service_accountsList ServiceAccounts
Storage (3 tools)
ToolDescription
k8s_get_pvcsList PersistentVolumeClaims with status, capacity, and storage class
k8s_get_pvsList PersistentVolumes with capacity and bound claims
k8s_get_storage_classesList StorageClasses with provisioner and default status
Networking (1 tool)
ToolDescription
k8s_get_network_policiesList NetworkPolicies with pod selectors and rule counts
Job Management (2 tools)
ToolDescription
k8s_get_jobsList Jobs with completion status and timing
k8s_get_cronjobsList CronJobs with schedule, suspend status, and last run

Usage Examples

Cluster Health Check
Give me a health overview of my production cluster.
Multi-Cluster Comparison
Compare the number of pods running in the 'backend' namespace between my 'prod' and 'staging' contexts.
Diagnose Application Issues
My deployment 'my-app' in the 'production' namespace is having issues. Can you diagnose what's wrong?
Scale Resources
Scale the 'backend' deployment in the 'default' namespace to 5 replicas.
Resource Management
Delete the failed job 'data-migration-v1' in the 'batch' namespace.
Create a new namespace called 'staging' with the label environment=staging.
Secret Management
List all secrets in the 'production' namespace and show me the keys in the 'api-credentials' secret.
RBAC Inspection
Show me all role bindings in the 'default' namespace -- who has access to what?
Storage Overview
List all PVCs in the cluster and show which ones are Pending.
Node Maintenance
Cordon node 'worker-3', drain it ignoring DaemonSets, then uncordon when maintenance is complete.
Rollback Deployment
Roll back the 'api-gateway' deployment in the 'services' namespace to the previous version.
Execute in Pod
Run 'ls -la /app/config' inside the 'app' container of pod 'web-app-xyz' in the 'default' namespace.
Create Resources
Create a new deployment with this YAML:
apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  namespace: default
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.27
        ports:
        - containerPort: 80

Architecture

k8s-multicluster-mcp/
├── app.py                          # MCP server entry point & tool registration
├── src/
│   ├── utils/
│   │   ├── k8s_client.py           # Multi-cluster client with context caching
│   │   └── pluralize.py            # Kubernetes resource kind pluralization
│   └── tools/
│       ├── contexts.py             # Context listing
│       ├── namespaces.py           # Namespace listing
│       ├── namespace_management.py # Namespace create/delete
│       ├── nodes.py                # Node listing
│       ├── pods.py                 # Resource listing & pod logs
│       ├── resources.py            # Single resource retrieval
│       ├── events.py               # Event listing
│       ├── describe.py             # kubectl describe equivalent
│       ├── api_discovery.py        # API & CRD discovery
│       ├── metrics.py              # Node/pod metrics (top)
│       ├── diagnosis.py            # Application diagnostics
│       ├── cluster_health.py       # Cluster health summary
│       ├── rollouts.py             # Rollout management
│       ├── scaling.py              # Scale & autoscale & resource updates
│       ├── resource_management.py  # Create, apply, patch, label, annotate
│       ├── delete_resource.py      # Resource deletion
│       ├── workload_management.py  # Expose, run pod, set resources
│       ├── node_management.py      # Cordon, drain, taint
│       ├── pod_operations.py       # Exec into pods
│       ├── secret_configmap.py     # Secret & ConfigMap management
│       ├── rbac.py                 # Roles, bindings, service accounts
│       ├── storage_network.py      # PVC, PV, StorageClass, NetworkPolicy
│       └── job_management.py       # Jobs & CronJobs
├── requirements.txt
├── pyproject.toml
├── Dockerfile
└── smithery.yaml

What's New in v2.0.0

  • 20+ new tools: Resource deletion, namespace management, secrets & configmaps, RBAC inspection, storage & network visibility, job management, cluster health summary, pod creation
  • Bug fixes: Fixed resource pluralization (e.g., Ingress -> ingresses not ingresss), fixed pod exec command splitting to handle quoted arguments, fixed timeout parameter handling
  • Performance: Context discovery now uses a 30-second TTL cache with direct context-to-file mapping, eliminating redundant file scanning
  • Improved tool descriptions: All 60+ tools have detailed descriptions for better AI assistant integration
  • Dependency cleanup: Removed unused fastapi and uvicorn dependencies
  • Security: Namespace deletion protects system namespaces; secret values hidden by default

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.