About Mcp K8s Eye

What is Mcp K8s Eye: Monitor Workloads & Streamline Clusters?

Mcp K8s Eye is a purpose-built tool designed to simplify Kubernetes cluster management and workload analysis. It provides a unified interface for monitoring pod statuses, scaling deployments, troubleshooting services, and maintaining cluster health. By consolidating essential operations into a single workflow, it reduces the cognitive load for operators managing complex environments.

How to use Mcp K8s Eye: Monitor Workloads & Streamline Clusters?

Prerequisites include Go 1.23+ and a configured kubectl context. Begin by cloning the repository:

git clone https://github.com/wenhuwang/mcp-k8s-eye.git
cd mcp-k8s-eye
go build -o mcp-k8s-eye

Integrate the tool into your workflow by configuring the path in your orchestration system:

{
  "mcpServers": {
    "kubernetes": {
      "command": "/path/to/mcp-k8s-eye",
      "env": {"HOME": "/home/user"}
    }
  }
}

Mcp K8s Eye Features

Key Features of Mcp K8s Eye: Monitor Workloads & Streamline Clusters?

Core management functions include:

Pod operations: List, execute commands, retrieve logs, and perform deletions
Deployment control: Scale replicas, inspect rollout statuses, and manage lifecycle events
Service visibility: Validate endpoints and troubleshoot network configurations

Analysis capabilities currently support pods, deployments, and services. Planned enhancements will extend these capabilities to StatefulSets, DaemonSets, and ingress resources, with cluster-wide analysis tools under development.

Use cases of Mcp K8s Eye: Monitor Workloads & Streamline Clusters?

Rapid incident response: Identify misconfigured pods or failed deployments using structured analysis outputs
Resource optimization: Scale deployments dynamically based on observed load patterns
Compliance checks: Validate service configurations against predefined security policies
Cluster onboarding: Automate initial setup and validation of critical components

Mcp K8s Eye FAQ

FAQ from Mcp K8s Eye: Monitor Workloads & Streamline Clusters?

Q: Does the tool support multi-cluster environments?
A: Yes, configure separate instances with distinct kubectl contexts.

Q: Are there RBAC requirements?
A: The service account must have permissions matching the managed resources (e.g., pod/exec for debug sessions).

Q: When will StatefulSet analysis be available?
A: Beta support is targeted for the Q2 2024 release.

Content

mcp-k8s-eye

mcp-k8s-eye is a tool that can manage kubernetes cluster and analyze workload status.

Requirements

Go 1.23 or higher
kubectl configured

Installation

# clone the repository
git clone https://github.com/wenhuwang/mcp-k8s-eye.git
cd mcp-k8s-eye

# build the binary
go build -o mcp-k8s-eye

Usage

{
  "mcpServers": {
    "kubernetes": {
      "command": "YOUR mcp-k8s-eye PATH",
      "env": {
        "HOME": "YOUR HOME DIR"
      },
    }
  }
}

Features

Connect to a Kubernetes cluster
Pod management capabilities (list, get, exec, logs, delete)
Deployment management capabilities (list, get, scale, delete)
Service management capabilities (list, get, delete)
StatefulSet management capabilities (list, get, delete)
DaemonSet management capabilities (list, get, delete)
Ingress management capabilities (list, get, delete)
Node management capabilities (list, get, delete)
Analyze pods
Analyze services
Analyze deployments
Analyze statefulsets
Analyze daemonsets
Analyze ingress
Analyze nodes
Analyze cluster

Mcp K8s Eye: Monitor Workloads & Streamline Clusters