Harvester MCP Server
About
Model Context Protocol (MCP) server for Harvester HCI
Details
- License
- Apache-2.0
Explore
- 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
- Go 1.23+
- Access to a Harvester cluster with a valid kubeconfig
go install github.com/starbops/harvester-mcp-server/cmd/harvester-mcp-server@latest
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"]
}
}
}
4. Restart Claude Desktop
5. The Harvester MCP tools should now be available to Claude
To add a new tool:
1. If it's a new resource type, add it to pkg/kubernetes/types.go
2. Implement formatters for the resource in one of the formatter files
3. Register the tool in pkg/mcp/server.go in the registerTools method using the unified resource handler
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.
Workflow
The following diagram illustrates how Harvester MCP Server bridges the gap between AI assistants and Harvester clusters:
graph LR;
subgraph "AI Assistants"
A[Claude Desktop] --> C[MCP Client];
B[Cursor IDE] --> C;
end
subgraph "Harvester MCP Server"
C --> D[MCP Server];
D --> E[Resource Handler];
E --> F[Formatter Registry];
F -->|Get Formatter| G[Core Resource Formatters];
F -->|Get Formatter| H[Harvester Resource Formatters];
end
subgraph "Kubernetes / Harvester"
G --> I[Kubernetes API];
H --> I;
I --> J[Harvester Cluster];
end
style A fill:#f9f,stroke:#333,stroke-width:2px;
style B fill:#f9f,stroke:#333,stroke-width:2px;
style D fill:#bbf,stroke:#333,stroke-width:2px;
style J fill:#bfb,stroke:#333,stroke-width:2px;
How It Works
1. LLM Integration: AI assistants like Claude Desktop and Cursor connect to Harvester MCP Server via the MCP protocol.
2. Request Processing: The MCP Server receives natural language requests from the AI assistants and translates them into specific Kubernetes operations.
3. Resource Handling: The Resource Handler identifies the resource type and operation being requested.
4. Formatter Selection: The Formatter Registry selects the appropriate formatter for the resource type.
5. API Interaction: The server interacts with the Kubernetes API of the Harvester cluster.
6. Response Formatting: Results are formatted into human-readable text optimized for LLM consumption.
7. User Presentation: Formatted responses are returned to the AI assistant to present to the user.
This architecture enables AI assistants to interact with Harvester clusters through natural language, making complex Kubernetes operations more accessible to users.
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
```bash
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
