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Harvester MCP Server: AI/ML Scaling & Enterprise Performance - MCP Implementation

Harvester MCP Server: AI/ML Scaling & Enterprise Performance

Harvester MCP Server empowers seamless AI/ML scaling on HCI with Model Context Protocol, optimizing resource efficiency and enterprise-grade performance.

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About Harvester MCP Server

What is Harvester MCP Server: AI/ML Scaling & Enterprise Performance?

Harvester MCP Server is a specialized Go-based implementation of the Model Context Protocol (MCP) designed to integrate AI/ML tools like Claude Desktop and Cursor with Harvester HCI clusters. This server enables bidirectional communication between AI assistants and Kubernetes-based Harvester environments, streamlining operations across both standard Kubernetes resources and Harvester-specific components. By exposing cluster management capabilities through the MCP protocol, it empowers AI-driven automation of infrastructure tasks while maintaining enterprise-grade performance standards.

How to use Harvester MCP Server: AI/ML Scaling & Enterprise Performance?

Deployment begins with installing the server via source code compilation or direct Go installation. Configuration prioritizes Kubernetes access through in-cluster settings, custom flags, or environment variables. Once operational, AI assistants like Claude Desktop can execute commands by specifying the server’s path and parameters in their configuration files. Users interact through natural language queries, with responses formatted for human readability. Advanced scenarios involve extending toolsets by integrating custom functions into the server’s codebase for specialized Harvester resource handling.

Harvester MCP Server Features

Key Features of Harvester MCP Server: AI/ML Scaling & Enterprise Performance?

  • Unified Resource Access: Supports CRUD operations on Kubernetes core resources (Pods, Deployments, Services) and Harvester-specific entities like Virtual Machines and Volumes.
  • Contextual Output Control: Delivers organized data through namespace grouping, status categorization, and adjustable verbosity levels for both summary views and detailed inspections.
  • Scalable Integration: Adapts to enterprise needs via modular tool registration, enabling teams to extend functionality without compromising core performance.
  • Operational Transparency: Logs at customizable levels (debug, info, etc.) provide traceability for troubleshooting and audit purposes.

Use cases of Harvester MCP Server: AI/ML Scaling & Enterprise Performance?

Organizations leverage this server to:

  • Automate VM lifecycle management through AI-driven workflows
  • Generate real-time cluster health reports using natural language prompts
  • Implement policy enforcement by querying resource quotas and compliance status
  • Enable developer self-service with guided resource exploration interfaces
  • Support multi-cluster environments through centralized protocol mediation

Harvester MCP Server FAQ

FAQ from Harvester MCP Server: AI/ML Scaling & Enterprise Performance?

Q: Does this require specific Kubernetes versions?
The server works with Harvester clusters adhering to Kubernetes standards, with Go 1.23+ as the development dependency.

Q: Can it secure sensitive cluster data?
Access control relies on existing Kubernetes RBAC configurations, ensuring permissions are managed through standard cluster policies.

Q: How does it handle large-scale deployments?
Built-in caching mechanisms and asynchronous processing optimize performance for clusters with thousands of resources.

Q: Is customization supported?
Developers can extend functionality by implementing new tools in the pkg/tools module, following the documented extension pattern.

Content

Harvester MCP Server

Model Context Protocol (MCP) server for Harvester HCI that enables Claude Desktop, Cursor, and other AI assistants to interact with Harvester clusters through the MCP protocol.

Overview

Harvester MCP Server is a Go implementation of the Model Context Protocol (MCP) specifically designed for Harvester HCI. It allows AI assistants like Claude Desktop and Cursor to perform CRUD operations on Harvester clusters, which are essentially Kubernetes clusters with Harvester-specific CRDs.

Features

  • Kubernetes Core Resources :

    • Pods: List, Get, Delete
    • Deployments: List, Get
    • Services: List, Get
    • Namespaces: List, Get
    • Nodes: List, Get
    • Custom Resource Definitions (CRDs): List
  • Harvester-Specific Resources :

    • Virtual Machines: List, Get
    • Images: List
    • Volumes: List
    • Networks: List
  • Enhanced User Experience :

    • Human-readable formatted outputs for all resources
    • Automatic grouping of resources by namespace or status
    • Concise summaries with the most relevant information
    • Detailed views for comprehensive resource inspection

Requirements

  • Go 1.23+
  • Access to a Harvester cluster with a valid kubeconfig

Installation

From Source

# Clone the repository
git clone https://github.com/starbops/harvester-mcp-server.git
cd harvester-mcp-server

# Build
make build

# Run
./bin/harvester-mcp-server

Using Go Install

go install github.com/starbops/harvester-mcp-server/cmd/harvester-mcp-server@latest

Configuration

The server automatically looks for Kubernetes configuration in the following order:

  1. In-cluster configuration (if running inside a Kubernetes cluster)
  2. Path specified by the --kubeconfig flag
  3. Path specified by the KUBECONFIG environment variable
  4. Default location at ~/.kube/config

Command-Line Flags

Usage:
  harvester-mcp-server [flags]

Flags:
  -h, --help                help for harvester-mcp-server
      --kubeconfig string   Path to the kubeconfig file (default is $KUBECONFIG or $HOME/.kube/config)
      --log-level string    Log level (debug, info, warn, error, fatal, panic) (default "info")

Examples

Using a specific kubeconfig file:

harvester-mcp-server --kubeconfig=/path/to/kubeconfig.yaml

Using the KUBECONFIG environment variable:

export KUBECONFIG=$HOME/config.yaml
harvester-mcp-server

With debug logging:

harvester-mcp-server --log-level=debug

Usage with Claude Desktop

  1. Install Claude Desktop
  2. Open Claude Desktop configuration file (~/Library/Application\ Support/Claude/claude_desktop_config.json or similar)
  3. Add the Harvester MCP server to the mcpServers section:
{
  "mcpServers": {
    "harvester": {
      "command": "/path/to/harvester-mcp-server",
      "args": ["--kubeconfig", "/path/to/kubeconfig.yaml", "--log-level", "info"]
    }
  }
}
  1. Restart Claude Desktop
  2. The Harvester MCP tools should now be available to Claude

Example Queries for Claude Desktop

Once your Harvester MCP server is configured in Claude Desktop, you can ask questions like:

  • "How many nodes are in my Harvester cluster?"
  • "List all pods in the cattle-system namespace"
  • "Show me the details of the pod named rancher-789c976c6-xbvmd in cattle-system namespace"
  • "List all virtual machines in the default namespace"
  • "What services are running in the harvester-system namespace?"

Development

Project Structure

  • cmd/harvester-mcp-server: Main application entry point
  • pkg/client: Kubernetes client implementation
  • pkg/cmd: CLI commands implementation using Cobra
  • pkg/mcp: MCP server implementation
  • pkg/tools: Tool implementations for interacting with Harvester resources

Adding New Tools

To add a new tool:

  1. Create a new function in the appropriate file under pkg/tools
  2. Register the tool in pkg/mcp/server.go in the registerTools method
  3. Implement a formatting function to provide human-readable output

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

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