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Kubectl

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kubectl-mcp-server enables natural language interaction with Kubernetes clusters, allowing users to manage and troubleshoot their infrastructure via AI assistants.

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Overview

kubectl-mcp-server enables natural language interaction with Kubernetes clusters, allowing users to manage and troubleshoot their infrastructure via AI assistants.

kubectl-mcp-server offers a convenient way to interact with Kubernetes using natural language. It's relatively safe for read-only operations and exploration. However, caution is advised when performing write operations, and proper RBAC configuration is crucial to mitigate risks.

Performance depends on the size and complexity of the Kubernetes cluster. Large clusters may experience slower response times.

Cost is primarily associated with the AI assistant's usage and any associated API calls. The kubectl-mcp-server itself has minimal resource requirements.

HybridPython818 starsMITReleased 2/4/2026CI/CD

Connections & Capabilities

Connects To

GitHubRedisDockerKubernetesVercel

Capabilities

ReadWriteExecAdmin

Client Compatibility

Claude Desktop

full

Cursor

full

Windsurf

full

VS Code (Copilot)

full

Quickstart

Install

npm install -g skillkit

Claude Desktop Config

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

Environment Variables

MCP_AUTH_JWKS_URIMCP_BROWSER_CDP_URL

Tools (5)

safe

get_pods

Retrieves a list of pods in a specified namespace.

Read-only operation, no modification of cluster state.

moderate

create_deployment

Creates a new deployment in the Kubernetes cluster.

Creates resources, but doesn't directly impact existing critical components.

moderate

scale_deployment

Scales the number of replicas for a given deployment.

Modifies the number of pods, potentially impacting application availability.

high

exec_pod

Executes a command inside a specified pod.

Allows arbitrary command execution within a pod, potentially leading to security breaches.

critical

delete_pod

Deletes a pod from the cluster.

Destructive operation that can cause service disruption.

Security

Auth Method

Token

Data Locality

hybrid

Known Risks

!Compromised AI assistant could lead to unauthorized cluster access
!Improperly configured RBAC could allow unintended actions
!Natural language ambiguity could result in incorrect commands
!Exposure of sensitive data in logs or AI assistant conversations
!Dependency on kubectl and its configuration introduces potential vulnerabilities

Safety Score

moderate risk

Positive

+Supports non-destructive mode to prevent accidental changes
+RBAC validation ensures actions are authorized
+Secret masking protects sensitive information
+Provides read-only access for safe exploration

Risks

-Can perform write operations on the cluster
-Requires kubectl to be configured correctly, which can be complex
-Potential for privilege escalation if RBAC is misconfigured
-Exposes Kubernetes API to AI assistant, increasing attack surface
-Incorrect natural language commands could lead to unintended actions

Use Cases

Troubleshooting

-Diagnosing pod crashes
-Identifying network connectivity issues
-Analyzing resource utilization

Deployment Management

-Creating new deployments
-Scaling existing deployments
-Rolling back deployments

Security Auditing

-Auditing RBAC permissions
-Checking for insecure secrets
-Identifying privileged pods

Cost Optimization

-Identifying resource wastage
-Analyzing cost optimization opportunities
-Recommending resource adjustments

Who Is This For?

Best For

Developers managing Kubernetes applications
DevOps engineers troubleshooting cluster issues
Security teams auditing cluster configurations
Teams seeking to optimize Kubernetes resource utilization
Users who prefer natural language interaction over kubectl commands

Not Ideal For

Critical production environments requiring strict control and auditability
Users with limited Kubernetes experience
Complex or highly customized Kubernetes deployments
Automated systems requiring predictable and deterministic behavior

Autonomy & Execution

Default ModeConfigurable

Destructive ToolsDry RunRollback

Autonomy depends on the AI assistant's capabilities and configuration. Exercise caution when granting full autonomy, especially with destructive tools.

StatelessYes

Retry LogicNot documented

ConcurrencySingle-threaded

Production Tip

Use dry-run mode and thorough testing in a non-production environment before applying changes to production clusters.

FAQ

What Kubernetes versions are supported?

kubectl-mcp-server supports the same Kubernetes versions as the underlying kubectl client.

How do I configure RBAC for kubectl-mcp-server?

Configure RBAC roles and role bindings to grant the necessary permissions to the service account used by kubectl-mcp-server.

Does kubectl-mcp-server support multi-cluster environments?

Yes, kubectl-mcp-server supports multi-cluster environments by leveraging kubectl's context switching capabilities.

How can I secure kubectl-mcp-server?

Enable authentication, use RBAC to restrict access, and regularly audit configurations.

What AI assistants are compatible with kubectl-mcp-server?

kubectl-mcp-server is compatible with any MCP-compatible AI assistant, including Claude, Cursor, and GitHub Copilot.

Can I use kubectl-mcp-server in a production environment?

Yes, but thorough testing and security precautions are recommended before deploying to production.

How do I troubleshoot issues with kubectl-mcp-server?

Check the logs for errors, verify kubectl configuration, and ensure the AI assistant is properly configured.

Key Features

Model Context Protocol (MCP) Integration - Allows seamless connection between Kubernetes clusters and any MCP-compliant AI assistant.

Natural Language Interface - Translates human language requests into specific Kubernetes API actions or queries.

Cross-Platform Compatibility - Supports flexible installation via NPM or pip for integration into various development environments.

Context-Aware Execution - Enables AI agents to interpret cluster state and provide actionable feedback based on real-time data.

Infrastructure Interaction - Facilitates programmatic management of cluster resources directly through AI-driven conversational workflows.

Use Cases & Problems Solved

Use Cases

•Use when you need to query the status of specific Kubernetes pods or deployments using natural language instead of complex kubectl command syntax.
•Perfect for debugging cluster issues in real-time by asking an AI assistant to fetch logs or describe failing resources.
•Ideal if you need to quickly audit cluster resource configurations or security settings without manually navigating the Kubernetes API.
•Great for automating repetitive cluster administration tasks by integrating Kubernetes operations directly into your AI development workflow.
•Use when onboarding junior developers who are not yet proficient in writing complex YAML manifests or kubectl filtering commands.
•Perfect for monitoring cluster performance metrics and resource utilization through conversational prompts during incident response.

Problems Solved

✓Eliminates the steep learning curve associated with memorizing complex kubectl command-line flags and syntax.
✓Reduces the time spent context-switching between your IDE, terminal, and Kubernetes documentation during infrastructure troubleshooting.
✓Removes the friction of manually parsing and filtering massive JSON or YAML outputs from the Kubernetes API.
✓Solves the difficulty of performing ad-hoc cluster resource inspection during high-pressure production outages.

Who It's For

DevOps Engineers performing daily cluster maintenance and resource management.Site Reliability Engineers (SREs) managing incident response and infrastructure health.Platform Engineers building internal developer platforms with AI-assisted interfaces.Backend Developers who need to verify deployment status without deep Kubernetes expertise.Cloud Infrastructure Consultants auditing multi-cluster environments for clients.

Metrics

Discovered2/14/2026

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Saved By0 users

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