MCP K8S Go: Model Context Protocol Server for Kubernetes Integration

Overview: What is MCP K8S Go?

MCP K8S Go is an advanced Golang-based server designed to facilitate interaction between AI applications and Kubernetes clusters through the Model Context Protocol (MCP). By leveraging MCP, this server enables seamless communication between AI tools like Claude Desktop and Kubernetes resources, ensuring a robust and standardized interface for data access and manipulation.

🔧 Core Features & MCP Capabilities

MCP K8S Go offers a wide array of core features that cater to the needs of both developers and end-users. These include:

List Kubernetes Contexts

MCP K8S Go allows you to list all available contexts in your Kubernetes cluster, providing a comprehensive view of environments where AI applications can operate.

List Kubernetes Namespaces

Users can explore namespaces within their Kubernetes clusters, offering insights into different logical groups or projects managed separately.

List and Get Any Kubernetes Resources

The server enables the listing and retrieval of various Kubernetes resources with custom mappings for common entities such as pods, services, deployments. Moreover, it supports any other type of resource that can be listed and accessed via MCP.

List Kubernetes Nodes

Detailed node information is available directly from the server, allowing for better management and monitoring of cluster nodes.

List Kubernetes Pods

Pods in Kubernetes clusters are easily accessible through this server, enabling detailed inspection and manipulation by AI applications.

Get Kubernetes Events

Retrieve live or historical events occurring within a Kubernetes namespace to maintain transparency and traceability across deployments.

Get Kubernetes Pod Logs

Access and manage pod logs directly from the server, facilitating troubleshooting and operational insight into running containers.

Run Command in Kubernetes Pod

Execute commands within specific pods, providing direct control over containerized applications hosted on Kubernetes clusters.

⚙️ MCP Architecture & Protocol Implementation

The architecture and protocol implementation of MCP K8S Go are designed to ensure interoperability with a wide range of AI clients. By adhering strictly to the Model Context Protocol, this server guarantees consistent and reliable communication between the client and backend infrastructure.

Protocol Flow Diagram

The following Mermaid diagram illustrates the flow of data in MCP K8S Go:

graph TB
    A[AI Application] -->|MCP Client| B[MCP K8S Server]
    B --> C[Kubernetes API]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Client Compatibility Matrix

The MCP K8S Go server is compatible with a variety of AI clients, including:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🚀 Getting Started with Installation

To get started using MCP K8S Go, follow these installation steps:

Using Smithery

Install the server automatically via Smithery:

npx -y @smithery/cli install @strowk/mcp-k8s --client claude

Using mcp-get

Install the server automatically via mcp-get:

npx @michaellatman/mcp-get@latest install @strowk/mcp-k8s

Manually with Prebuilt Binaries

Install the server manually:

From npm

Using Node.js and npm, you can easily preinstall the server:

npm install -g @strowk/mcp-k8s

Then check version by running mcp-k8s --version. Add configuration to claude_desktop_config.json as needed.

From GitHub Releases

Download a binary release from GitHub releases. Place the binary in your PATH and configure claude_desktop_config.json.

Building from Source

You need Golang installed to build the project:

go get github.com/strowk/mcp-k8s-go
go install github.com/strowk/mcp-k8s-go

Add configuration as described above.

Using Docker

The server is available on Docker Hub with multi-arch support. Use latest tag, for example:

docker run -i -v ~/.kube/config:/home/nonroot/.kube/config --rm mcpk8s/server:latest

Windows users might need to adjust paths when using Git Bash.

💡 Key Use Cases in AI Workflows

Use Case 1: Automated Kubernetes Cluster Management with MCP K8S Go

AI applications can leverage MCP K8S Go for automated management of Kubernetes clusters. By automating deployment, scaling, and monitoring tasks, enterprises can significantly reduce manual effort required by DevOps teams.

Technical Implementation: Developers add MCP K8S Go as a resource in their AI pipeline configuration. They define specific commands to create, scale, and destroy pods based on real-time application needs.

Use Case 2: Real-Time Pod Monitoring with Log Aggregation

AI applications can monitor pod logs in real-time using the MCP server, collecting operational data for immediate troubleshooting or logging aggregation without manual intervention.

Technical Implementation: A continuous log tailing mechanism is implemented within the AI application. When a new log entry is received via MCP communication, it triggers an automated alert system configured with MCP K8S Go.

🔌 Integration with MCP Clients

MCP K8S Go seamlessly integrates with several popular MCP clients:

Configuration Sample

Here’s a sample configuration for integrating MCP K8S Go with various clients:

{
  "mcpServers": {
    "mcp_k8s_go": {
      "command": "npx",
      "args": ["@strowk/mcp-k8s"]
    },
    "docker-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "-v",
        "~/.kube/config:/home/nonroot/.kube/config",
        "--rm",
        "mcpk8s/server:latest"
      ]
    }
  }
}

📊 Performance & Compatibility Matrix

MCP K8S Go is designed to be highly performant and compatible across different environments. It supports various Kubernetes distributions, ensuring broad adoption.

Performance Metrics

Response Time: <50ms for most requests.
Throughput: Up to 1000 operations per second under load testing conditions.

Compatibility Table

Environment	Support
Windows	Partial (requires specific setup)
MacOS	Full
Linux	Full

🛠️ Advanced Configuration & Security

Common Environment Variables

KUBECONFIG: Path to the configuration file for accessing Kubernetes clusters.
API_KEY: API key required for secure communication.

Security Considerations

Implement proper access controls via environment variables and config files.
Use encrypted methods for storing sensitive information like API keys.

FAQs

Q: How does MCP K8S Go ensure the security of data transfers?
A: MC K8S Go uses encryption protocols such as TLS to secure all communication channels, ensuring data confidentiality and integrity.
Q: Can other tools besides AI applications use MCP K8S Go?
A: Yes, any application or system that can conform to the Model Context Protocol (MCP) standards can utilize this server.
Q: How does MCP K8S Go handle scalability challenges in large clusters?
A: The server uses efficient caching and queuing mechanisms to manage high load scenarios, ensuring timely responses even during peak usage times.
Q: What level of support is available for deploying MCP K8S Go in production environments?
A: We provide detailed deployment guides and extensive documentation to assist with setup and maintenance in production settings.
Q: Can custom Kubernetes resources be integrated with MCP K8S Go?
A: Yes, MCP K8S Go supports integration of any custom Kubernetes resource types through the configuration options provided by MCP.

By providing robust integration capabilities along with detailed documentation and support, MCP K8S Go aims to empower developers and organizations in building advanced AI applications that seamlessly interact with Kubernetes ecosystems.

Mcp K8s Go