k8s-mcp 0.1.2

A Model Context Protocol (MCP) server for Kubernetes with 270+ tools, 8 resources, and 8 prompts

k8s-mcp

Control your entire Kubernetes infrastructure through natural language conversations with AI.
Talk to your clusters like you talk to a DevOps expert. Debug crashed pods, optimize costs, deploy applications, audit security, manage Helm charts, and visualize dashboards, all through natural language.

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Fork Notice

This project is forked from kubectl-mcp-server by Rohit Ghumare. We appreciate the original author's excellent work on building this powerful Kubernetes MCP server. This fork continues development under the new name k8s-mcp with independent releases.

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Installation

Quick Start with npx (Recommended - Zero Install)

# Run directly without installation - works instantly!
npx -y k8s-mcp

# Or install globally for faster startup
npm install -g k8s-mcp

Or install with pip (Python)

# Standard installation
pip install k8s-mcp

# With interactive UI dashboards (recommended)
pip install k8s-mcp[ui]

Prerequisites

• Python 3.9+ (for pip installation)
• Node.js 14+ (for npx installation)
• kubectl installed and configured
• Access to a Kubernetes cluster

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Table of Contents

• What Can You Do?
• Why k8s-mcp?
• Installation
• Getting Started
• Quick Setup with Your AI Assistant
• All Supported AI Assistants
• Complete Feature Set
• Using the CLI
• Advanced Configuration
• Optional Features
  • Interactive Dashboards
  • Browser Automation
• Enterprise
• In-Cluster Deployment
• Multi-Cluster Support
• Architecture
• Agent Skills
• Development & Testing
• Contributing
• Support & Community

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What Can You Do?

Simply ask your AI assistant in natural language:

"Why is my pod crashing?"

• Instant crash diagnosis with logs, events, and resource analysis
• Root cause identification with actionable recommendations

"Deploy a Redis cluster with 3 replicas"

• Creates deployment with best practices
• Configures services, persistent storage, and health checks

"Show me which pods are wasting resources"

• AI-powered cost optimization analysis
• Resource recommendations with potential savings

"Which services can't reach the database?"

• Network connectivity diagnostics with DNS resolution
• Service chain tracing from ingress to pods

"Audit security across all namespaces"

• RBAC permission analysis
• Secret security scanning and pod security policies

"Show me the cluster dashboard"

• Interactive HTML dashboards with live metrics
• Visual timeline of events and resource usage

253 powerful tools | 8 workflow prompts | 8 data resources | Works with all major AI assistants

Why k8s-mcp?

• Stop context-switching - Manage Kubernetes directly from your AI assistant conversations
• AI-powered diagnostics - Get intelligent troubleshooting, not just raw data
• Built-in cost optimization - Identify waste and get actionable savings recommendations
• Enterprise-ready - OAuth 2.1 auth, RBAC validation, non-destructive mode, secret masking
• Zero learning curve - Natural language instead of memorizing kubectl commands
• Universal compatibility - Works with Claude, Cursor, Windsurf, Copilot, and 15+ other AI tools
• Visual insights - Interactive dashboards and browser automation for web-based tools
• Production-grade - Deploy in-cluster, multi-cluster support, active maintenance

From debugging crashed pods to optimizing cluster costs, k8s-mcp is your AI-powered DevOps companion.

Getting Started

1. Test the Server (Optional)

Before integrating with your AI assistant, verify the installation:

# Check if kubectl is configured
kubectl cluster-info

# Test the MCP server directly
k8s-mcp info

# List all available tools
k8s-mcp tools

# Try calling a tool
k8s-mcp call get_pods '{"namespace": "kube-system"}'

2. Connect to Your AI Assistant

Choose your favorite AI assistant and add the configuration:

Quick Setup with Your AI Assistant

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "k8s-mcp"]
    }
  }
}

Cursor AI

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "k8s-mcp"]
    }
  }
}

Windsurf

Add to ~/.config/windsurf/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "k8s-mcp"]
    }
  }
}

Using Python Instead of npx

{
  "mcpServers": {
    "kubernetes": {
      "command": "python",
      "args": ["-m", "kubectl_mcp_tool.mcp_server"],
      "env": {
        "KUBECONFIG": "/path/to/.kube/config"
      }
    }
  }
}

More integrations: GitHub Copilot, Goose, Gemini CLI, Roo Code, and 15+ other clients — see full configuration guide below.

3. Restart Your AI Assistant

After adding the configuration, restart your AI assistant (Claude Desktop, Cursor, etc.) to load the MCP server.

4. Try These Commands

Start a conversation with your AI assistant and try these:

Troubleshooting:

"Show me all pods in the kube-system namespace"
"Why is the nginx-deployment pod crashing?"
"Diagnose network connectivity issues in the default namespace"

Deployments:

"Create a deployment for nginx with 3 replicas"
"Scale my frontend deployment to 5 replicas"
"Roll back the api-server deployment to the previous version"

Cost & Optimization:

"Which pods are using the most resources?"
"Show me idle resources that are wasting money"
"Analyze cost optimization opportunities in the production namespace"

Security:

"Audit RBAC permissions in all namespaces"
"Check for insecure secrets and configurations"
"Show me pods running with privileged access"

Helm:

"List all Helm releases in the cluster"
"Install Redis from the Bitnami chart repository"
"Show me the values for my nginx-ingress Helm release"

Multi-Cluster:

"List all available Kubernetes contexts"
"Switch to the production cluster context"
"Show me cluster information and version"

MCP Client Compatibility

Works seamlessly with all MCP-compatible AI assistants:

Client	Status	Client	Status
Claude Desktop	Native	Claude Code	Native
Cursor	Native	Windsurf	Native
GitHub Copilot	Native	OpenAI Codex	Native
Gemini CLI	Native	Goose	Native
Roo Code	Native	Kilo Code	Native
Amp	Native	Trae	Native
OpenCode	Native	Kiro CLI	Native
Antigravity	Native	Clawdbot	Native
Droid (Factory)	Native	Any MCP Client	Compatible

All Supported AI Assistants

Claude Code

Add to ~/.config/claude-code/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "k8s-mcp"]
    }
  }
}

GitHub Copilot (VS Code)

Add to VS Code settings.json:

{
  "mcp": {
    "servers": {
      "kubernetes": {
        "command": "npx",
        "args": ["-y", "k8s-mcp"]
      }
    }
  }
}

Goose

Add to ~/.config/goose/config.yaml:

extensions:
  kubernetes:
    command: npx
    args:
      - -y
      - k8s-mcp

Gemini CLI

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "k8s-mcp"]
    }
  }
}

Roo Code / Kilo Code

Add to ~/.config/roo-code/mcp.json or ~/.config/kilo-code/mcp.json:

{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["-y", "k8s-mcp"]
    }
  }
}

Complete Feature Set

253 MCP Tools for Complete Kubernetes Management

Category	Tools
Pods	get_pods, get_logs, get_pod_events, check_pod_health, exec_in_pod, cleanup_pods, get_pod_conditions, get_previous_logs
Deployments	get_deployments, create_deployment, scale_deployment, kubectl_rollout, restart_deployment
Workloads	get_statefulsets, get_daemonsets, get_jobs, get_replicasets
Services & Networking	get_services, get_ingress, get_endpoints, diagnose_network_connectivity, check_dns_resolution, trace_service_chain
Storage	get_persistent_volumes, get_pvcs, get_storage_classes
Config	get_configmaps, get_secrets, get_resource_quotas, get_limit_ranges
Cluster	get_nodes, get_namespaces, get_cluster_info, get_cluster_version, health_check, get_node_metrics, get_pod_metrics
RBAC & Security	get_rbac_roles, get_cluster_roles, get_service_accounts, audit_rbac_permissions, check_secrets_security, get_pod_security_info, get_admission_webhooks
CRDs	get_crds, get_priority_classes
Helm Releases	helm_list, helm_status, helm_history, helm_get_values, helm_get_manifest, helm_get_notes, helm_get_hooks, helm_get_all
Helm Charts	helm_show_chart, helm_show_values, helm_show_readme, helm_show_crds, helm_show_all, helm_search_repo, helm_search_hub
Helm Repos	helm_repo_list, helm_repo_add, helm_repo_remove, helm_repo_update
Helm Operations	install_helm_chart, upgrade_helm_chart, uninstall_helm_chart, helm_rollback, helm_test, helm_template, helm_template_apply
Helm Development	helm_create, helm_lint, helm_package, helm_pull, helm_dependency_list, helm_dependency_update, helm_dependency_build, helm_version, helm_env
Context	get_current_context, switch_context, list_contexts, list_kubeconfig_contexts
Diagnostics	diagnose_pod_crash, detect_pending_pods, get_evicted_pods, compare_namespaces
Operations	kubectl_apply, kubectl_create, kubectl_describe, kubectl_patch, delete_resource, kubectl_cp, backup_resource, label_resource, annotate_resource, taint_node, wait_for_condition
Autoscaling	get_hpa, get_pdb
Cost Optimization	get_resource_recommendations, get_idle_resources, get_resource_quotas_usage, get_cost_analysis, get_overprovisioned_resources, get_resource_trends, get_namespace_cost_allocation, optimize_resource_requests
Advanced	kubectl_generic, kubectl_explain, get_api_resources, port_forward, get_resource_usage, node_management
UI Dashboards	show_pod_logs_ui, show_pods_dashboard_ui, show_resource_yaml_ui, show_cluster_overview_ui, show_events_timeline_ui, render_k8s_dashboard_screenshot
GitOps (Flux/Argo)	gitops_apps_list, gitops_app_get, gitops_app_sync, gitops_app_status, gitops_sources_list, gitops_source_get, gitops_detect_engine
Cert-Manager	certs_list, certs_get, certs_issuers_list, certs_issuer_get, certs_renew, certs_status_explain, certs_challenges_list, certs_requests_list, certs_detect
Policy (Kyverno/Gatekeeper)	policy_list, policy_get, policy_violations_list, policy_explain_denial, policy_audit, policy_detect
Backup (Velero)	backup_list, backup_get, backup_create, backup_delete, restore_list, restore_create, restore_get, backup_locations_list, backup_schedules_list, backup_schedule_create, backup_detect
KEDA Autoscaling	keda_scaledobjects_list, keda_scaledobject_get, keda_scaledjobs_list, keda_triggerauths_list, keda_triggerauth_get, keda_hpa_list, keda_detect
Cilium/Hubble	cilium_policies_list, cilium_policy_get, cilium_endpoints_list, cilium_identities_list, cilium_nodes_list, cilium_status, hubble_flows_query, cilium_detect
Argo Rollouts/Flagger	rollouts_list, rollout_get, rollout_status, rollout_promote, rollout_abort, rollout_retry, rollout_restart, analysis_runs_list, flagger_canaries_list, flagger_canary_get, rollouts_detect
Cluster API	capi_clusters_list, capi_cluster_get, capi_machines_list, capi_machine_get, capi_machinedeployments_list, capi_machinedeployment_scale, capi_machinesets_list, capi_machinehealthchecks_list, capi_clusterclasses_list, capi_cluster_kubeconfig, capi_detect
KubeVirt VMs	kubevirt_vms_list, kubevirt_vm_get, kubevirt_vmis_list, kubevirt_vm_start, kubevirt_vm_stop, kubevirt_vm_restart, kubevirt_vm_pause, kubevirt_vm_unpause, kubevirt_vm_migrate, kubevirt_datasources_list, kubevirt_instancetypes_list, kubevirt_datavolumes_list, kubevirt_detect
Istio/Kiali	istio_virtualservices_list, istio_virtualservice_get, istio_destinationrules_list, istio_gateways_list, istio_peerauthentications_list, istio_authorizationpolicies_list, istio_proxy_status, istio_analyze, istio_sidecar_status, istio_detect
vCluster (vind)	vind_detect_tool, vind_list_clusters_tool, vind_status_tool, vind_get_kubeconfig_tool, vind_logs_tool, vind_create_cluster_tool, vind_delete_cluster_tool, vind_pause_tool, vind_resume_tool, vind_connect_tool, vind_disconnect_tool, vind_upgrade_tool, vind_describe_tool, vind_platform_start_tool
kind (K8s in Docker)	kind_detect_tool, kind_version_tool, kind_list_clusters_tool, kind_get_nodes_tool, kind_get_kubeconfig_tool, kind_export_logs_tool, kind_cluster_info_tool, kind_node_labels_tool, kind_create_cluster_tool, kind_delete_cluster_tool, kind_delete_all_clusters_tool, kind_load_image_tool, kind_load_image_archive_tool, kind_build_node_image_tool, kind_set_kubeconfig_tool

MCP Resources

Access Kubernetes data as browsable resources:

Resource URI	Description
kubeconfig://contexts	List all available kubectl contexts
kubeconfig://current-context	Get current active context
namespace://current	Get current namespace
namespace://list	List all namespaces
cluster://info	Get cluster information
cluster://nodes	Get detailed node information
cluster://version	Get Kubernetes version
cluster://api-resources	List available API resources
manifest://deployments/{ns}/{name}	Get deployment YAML
manifest://services/{ns}/{name}	Get service YAML
manifest://pods/{ns}/{name}	Get pod YAML
manifest://configmaps/{ns}/{name}	Get ConfigMap YAML
manifest://secrets/{ns}/{name}	Get secret YAML (data masked)
manifest://ingresses/{ns}/{name}	Get ingress YAML

MCP Prompts

Pre-built workflow prompts for common Kubernetes operations:

Prompt	Description
troubleshoot_workload	Comprehensive troubleshooting guide for pods/deployments
deploy_application	Step-by-step deployment workflow
security_audit	Security scanning and RBAC analysis workflow
cost_optimization	Resource optimization and cost analysis workflow
disaster_recovery	Backup and recovery planning workflow
debug_networking	Network debugging for services and connectivity
scale_application	Scaling guide with HPA/VPA best practices
upgrade_cluster	Kubernetes cluster upgrade planning

Key Capabilities

• 253 Powerful Tools - Complete Kubernetes management from pods to security
• 8 AI Workflow Prompts - Pre-built workflows for common operations
• 8 MCP Resources - Browsable Kubernetes data exposure
• 6 Interactive Dashboards - HTML UI tools for visual cluster management
• 26 Browser Tools - Web automation with cloud provider support
• 107 Ecosystem Tools - GitOps, Cert-Manager, Policy, Backup, KEDA, Cilium, Rollouts, CAPI, KubeVirt, Istio, vCluster
• Multi-Transport - stdio, SSE, HTTP, streamable-http
• Security First - Non-destructive mode, secret masking, RBAC validation
• Advanced Diagnostics - AI-powered troubleshooting and cost optimization
• Multi-Cluster - Target any cluster via context parameter in every tool
• Full Helm v3 - Complete chart lifecycle management
• Powerful CLI - Shell-friendly tool discovery and direct calling
• Cloud Native - Deploy in-cluster

Using the CLI

The built-in CLI lets you explore and test tools without an AI assistant:

# List all tools with descriptions
k8s-mcp tools -d

# Search for pod-related tools
k8s-mcp grep "*pod*"

# Show specific tool schema
k8s-mcp tools get_pods

# Call a tool directly
k8s-mcp call get_pods '{"namespace": "kube-system"}'

# Pipe JSON from stdin
echo '{"namespace": "default"}' | k8s-mcp call get_pods

# Check dependencies
k8s-mcp doctor

# Show/switch Kubernetes context
k8s-mcp context
k8s-mcp context minikube

# List resources and prompts
k8s-mcp resources
k8s-mcp prompts

# Show server info
k8s-mcp info

CLI Features

• Structured errors: Actionable error messages with suggestions
• Colorized output: Human-readable with JSON mode for scripting (--json)
• NO_COLOR support: Respects NO_COLOR environment variable
• Stdin support: Pipe JSON arguments to commands

Advanced Configuration

Transport Modes

The server supports multiple transport protocols:

# stdio (default) - Best for Claude Desktop, Cursor, Windsurf
k8s-mcp
# or: python -m kubectl_mcp_tool.mcp_server

# SSE - Server-Sent Events for web clients
k8s-mcp --transport sse --port 8000

# HTTP - Standard HTTP for REST clients
k8s-mcp --transport http --port 8000

# streamable-http - For agentgateway integration
k8s-mcp --transport streamable-http --port 8000

Transport Options:

• --transport: Choose from stdio, sse, http, streamable-http (default: stdio)
• --host: Bind address (default: 0.0.0.0)
• --port: Port for network transports (default: 8000)
• --disable-destructive (or --non-destructive): Block destructive operations
• --read-only: Block all write operations

Environment Variables

Core Settings:

Variable	Description	Default
KUBECONFIG	Path to kubeconfig file	~/.kube/config
MCP_DEBUG	Enable verbose logging	false
MCP_LOG_FILE	Log file path	None (stdout)

Authentication (Enterprise):

Variable	Description	Default
MCP_AUTH_ENABLED	Enable OAuth 2.1 authentication	false
MCP_AUTH_ISSUER	OAuth 2.0 Authorization Server URL	-
MCP_AUTH_JWKS_URI	JWKS endpoint URL	Auto-derived
MCP_AUTH_AUDIENCE	Expected token audience	k8s-mcp
MCP_AUTH_REQUIRED_SCOPES	Required OAuth scopes	mcp:tools

Browser Automation (Optional):

Variable	Description	Default
MCP_BROWSER_ENABLED	Enable browser automation tools	false
MCP_BROWSER_PROVIDER	Cloud provider (browserbase/browseruse)	None
MCP_BROWSER_PROFILE	Persistent profile path	None
MCP_BROWSER_CDP_URL	Remote CDP WebSocket URL	None
MCP_BROWSER_PROXY	Proxy server URL	None

Optional: Interactive Dashboards (6 UI Tools)

Get beautiful HTML dashboards for visual cluster management.

Installation:

# Install with UI support
pip install k8s-mcp[ui]

6 Dashboard Tools:

• show_pods_dashboard_ui - Real-time pod status table
• show_pod_logs_ui - Interactive log viewer with search
• show_cluster_overview_ui - Complete cluster dashboard
• show_events_timeline_ui - Events timeline with filtering
• show_resource_yaml_ui - YAML viewer with syntax highlighting
• render_k8s_dashboard_screenshot - Export dashboards as PNG

Features:

• Dark theme optimized for terminals (Catppuccin)
• Graceful fallback to JSON for incompatible clients
• Screenshot rendering for universal compatibility
• Zero external dependencies

Works With: Goose, LibreChat, Nanobot (full HTML UI) | Claude Desktop, Cursor, others (JSON + screenshots)

Optional: Browser Automation (26 Tools)

Automate web-based Kubernetes operations with browser integration.

Quick Setup:

# Enable browser tools
export MCP_BROWSER_ENABLED=true
k8s-mcp

What You Can Do:

• Test deployed apps via Ingress URLs
• Screenshot Grafana, ArgoCD, or any K8s dashboard
• Automate cloud console operations (EKS, GKE, AKS)
• Health check web applications
• Export monitoring dashboards as PDF
• Test authentication flows with persistent sessions

Advanced Features:

• Cloud providers: Browserbase, Browser Use
• Persistent browser profiles
• Remote CDP connections
• Session management

Enterprise: OAuth 2.1 Authentication

Secure your MCP server with OAuth 2.1 authentication (RFC 9728).

export MCP_AUTH_ENABLED=true
export MCP_AUTH_ISSUER=https://your-idp.example.com
export MCP_AUTH_AUDIENCE=k8s-mcp
k8s-mcp --transport http --port 8000

Supported Identity Providers: Okta, Auth0, Keycloak, Microsoft Entra ID, Google OAuth, and any OIDC-compliant provider.

Use Case: Multi-tenant environments, compliance requirements, audit logging.

In-Cluster Deployment

Option 1: Standard Kubernetes

Deploy with kubectl/kustomize:

# Using kustomize (recommended)
kubectl apply -k deploy/kubernetes/

# Or individual manifests
kubectl apply -f deploy/kubernetes/namespace.yaml
kubectl apply -f deploy/kubernetes/rbac.yaml
kubectl apply -f deploy/kubernetes/deployment.yaml
kubectl apply -f deploy/kubernetes/service.yaml

# Access via port-forward
kubectl port-forward -n kubectl-mcp svc/k8s-mcp 8000:8000

See deploy/ directory for all manifests and configuration options.

Architecture

+-----------------+     +------------------+     +-----------------+
|   AI Assistant  |---->|   MCP Server     |---->|  Kubernetes API |
| (Claude/Cursor) |<----| (k8s-mcp)        |<----|    (kubectl)    |
+-----------------+     +------------------+     +-----------------+

The MCP server implements the Model Context Protocol, translating natural language requests into kubectl operations.

Modular Structure

kubectl_mcp_tool/
├── mcp_server.py          # Main server (FastMCP, transports)
├── tools/                  # 253 MCP tools organized by category
│   ├── pods.py            # Pod management & diagnostics
│   ├── deployments.py     # Deployments, StatefulSets, DaemonSets
│   ├── core.py            # Namespaces, ConfigMaps, Secrets
│   ├── cluster.py         # Context/cluster management
│   ├── networking.py      # Services, Ingress, NetworkPolicies
│   ├── storage.py         # PVCs, StorageClasses, PVs
│   ├── security.py        # RBAC, ServiceAccounts, PodSecurity
│   ├── helm.py            # Complete Helm v3 operations
│   ├── operations.py      # kubectl apply/patch/describe/etc
│   ├── diagnostics.py     # Metrics, namespace comparison
│   ├── cost.py            # Resource optimization & cost analysis
│   ├── ui.py              # MCP-UI interactive dashboards
│   ├── gitops.py          # GitOps (Flux/ArgoCD)
│   ├── certs.py           # Cert-Manager
│   ├── policy.py          # Policy (Kyverno/Gatekeeper)
│   ├── backup.py          # Backup (Velero)
│   ├── keda.py            # KEDA autoscaling
│   ├── cilium.py          # Cilium/Hubble network observability
│   ├── rollouts.py        # Argo Rollouts/Flagger
│   ├── capi.py            # Cluster API
│   ├── kubevirt.py        # KubeVirt VMs
│   ├── kiali.py           # Istio/Kiali service mesh
│   └── vind.py            # vCluster (virtual clusters)
├── resources/              # 8 MCP Resources for data exposure
├── prompts/                # 8 MCP Prompts for workflows
└── cli/                    # CLI interface

Agent Skills (25 Skills for AI Coding Agents)

Extend your AI coding agent with Kubernetes expertise using our Agent Skills library. Skills provide specialized knowledge and workflows that agents can load on demand.

Quick Install

# Copy all skills to Claude
cp -r kubernetes-skills/claude/* ~/.claude/skills/

# Or install specific skills
cp -r kubernetes-skills/claude/k8s-helm ~/.claude/skills/

Available Skills (25)

Category	Skills
Core Resources	k8s-core, k8s-networking, k8s-storage
Workloads	k8s-deploy, k8s-operations, k8s-helm
Observability	k8s-diagnostics, k8s-troubleshoot, k8s-incident
Security	k8s-security, k8s-policy, k8s-certs
GitOps	k8s-gitops, k8s-rollouts
Scaling	k8s-autoscaling, k8s-cost, k8s-backup
Multi-Cluster	k8s-multicluster, k8s-capi, k8s-kubevirt, k8s-vind
Networking	k8s-service-mesh, k8s-cilium
Tools	k8s-browser, k8s-cli

Convert to Other Agents

Use SkillKit to convert skills to your preferred AI agent format:

npm install -g skillkit

# Convert to Cursor format
skillkit translate kubernetes-skills/claude --to cursor --output .cursor/rules/

# Convert to Codex format
skillkit translate kubernetes-skills/claude --to codex --output ./

Supported agents: Claude, Cursor, Codex, Gemini CLI, GitHub Copilot, Goose, Windsurf, Roo, Amp, and more.

See kubernetes-skills/README.md for full documentation.

Multi-Cluster Support

Seamlessly manage multiple Kubernetes clusters through natural language. Every tool supports an optional context parameter to target any cluster without switching contexts.

Context Parameter

Most kubectl-backed tools accept an optional context parameter to target specific clusters. Note: vCluster (vind) and kind tools run via their local CLIs and do not accept the context parameter.

Talk to your AI assistant:

"List pods in the production cluster"
"Get deployments from staging context"
"Show logs from the api-pod in the dev cluster"
"Compare namespaces between production and staging clusters"

Direct tool calls with context:

# Target a specific cluster context
k8s-mcp call get_pods '{"namespace": "default", "context": "production"}'

# Get deployments from staging
k8s-mcp call get_deployments '{"namespace": "app", "context": "staging"}'

# Install Helm chart to production cluster
k8s-mcp call install_helm_chart '{"name": "redis", "chart": "bitnami/redis", "namespace": "cache", "context": "production"}'

# Compare resources across clusters
k8s-mcp call compare_namespaces '{"namespace1": "prod-ns", "namespace2": "staging-ns", "context": "production"}'

Context Management

Talk to your AI assistant:

"List all available Kubernetes contexts"
"Switch to the production cluster"
"Show me details about the staging context"
"What's the current cluster I'm connected to?"

Or use the CLI directly:

k8s-mcp context                    # Show current context
k8s-mcp context production         # Switch context
k8s-mcp call list_contexts_tool    # List all contexts via MCP

How It Works

• If context is omitted, the tool uses your current kubectl context
• If context is specified, the tool targets that cluster directly
• Response includes "context": "production" or "context": "current" for clarity
• Works with all kubeconfig setups and respects KUBECONFIG environment variable
• No need to switch contexts for cross-cluster operations

Development & Testing

Setup Development Environment

# Clone the repository
git clone https://github.com/xuyun-io/k8s-mcp.git
cd k8s-mcp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt

Running Tests

# Run all tests
pytest tests/ -v

# Run specific test file
pytest tests/test_tools.py -v

# Run with coverage
pytest tests/ --cov=kubectl_mcp_tool --cov-report=html

# Run only unit tests
pytest tests/ -v -m unit

Test Structure

tests/
├── __init__.py          # Test package
├── conftest.py          # Shared fixtures and mocks
├── test_tools.py        # Unit tests for 253 MCP tools
├── test_resources.py    # Tests for 8 MCP Resources
├── test_prompts.py      # Tests for 8 MCP Prompts
└── test_server.py       # Server initialization tests

234 tests covering: tool registration, resource exposure, prompt generation, server initialization, non-destructive mode, secret masking, error handling, transport methods, CLI commands, browser automation, and ecosystem tools.

Code Quality

# Format code
black kubectl_mcp_tool tests

# Sort imports
isort kubectl_mcp_tool tests

# Lint
flake8 kubectl_mcp_tool tests

# Type checking
mypy kubectl_mcp_tool

Contributing

We welcome contributions! Whether it's bug reports, feature requests, documentation improvements, or code contributions.

Ways to contribute:

• Report bugs via GitHub Issues
• Suggest features or improvements
• Improve documentation
• Submit pull requests
• Star the project if you find it useful!

Development setup: See Development & Testing section above.

Before submitting a PR:

1. Run tests: pytest tests/ -v
2. Format code: black kubectl_mcp_tool tests
3. Check linting: flake8 kubectl_mcp_tool tests

Support & Community

• Documentation
• GitHub Issues
• Feature Requests

License

MIT License - see LICENSE for details.

Links & Resources

Package Repositories:

• PyPI Package
• npm Package

Project:

• GitHub Repository
• Issue Tracker
• Changelog

Ecosystem:

• Model Context Protocol
• Kubernetes Documentation

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Made with for the Kubernetes and AI community

If k8s-mcp makes your DevOps life easier, give it a star on GitHub!