What are the prerequisites for using this server?
Python 3.11+, access to a Kubernetes cluster, kubectl configured locally, and UV installed.
@ductnn
Basic information
A lightweight MCP server that provides natural language processing and API access to Kubernetes clusters, combining both kubectl commands and Kubernetes Python client.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-kubernetes-server-ductnn": {
"command": "uv",
"args": [
"venv",
".venv"
]
}
}
}Tool list not available yet
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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
A lightweight MCP server that provides natural language processing and API access to Kubernetes clusters, combining kubectl commands with the Kubernetes Python client. It is intended for developers and operators who want to manage Kubernetes resources through AI assistants or direct API calls.
Install Python 3.11+, ensure kubectl is configured locally, and install UV. Clone the repository, create a virtual environment with UV, and install dependencies. Run the server with uv run main.py or configure it as an MCP server in tools like Claude Desktop by editing the claude_desktop_config.json.
Python 3.11+, access to a Kubernetes cluster, kubectl configured locally, and UV installed.
The server uses a natural language processor (nl_processor) that converts phrases like “Show me all pods” into equivalent kubectl commands. If a query is unsupported, it falls back to a general resource listing.
Yes, the server provides full CRUD operations via dedicated API endpoints for pods, deployments, and namespaces, including graceful deletion and force delete options.
Edit your `claude_desktop_config.json` to add a “kubernetes” MCP server entry pointing to the `uv` command and the project directory, then restart Claude Desktop.
It supports both execution modes. kubectl must be configured locally, but the server also uses the official Kubernetes Python SDK for operations.
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