Proxmox MCP Server

Overview

The Proxmox MCP Server bridges the gap between large‑language models (LLMs) and virtual infrastructure by exposing Proxmox VE’s rich API through the Model Context Protocol. By translating native Proxmox data into MCP context endpoints, the server allows AI assistants to query cluster status, node details, and virtual machine (VM) information in a single, consistent request format. This eliminates the need for custom integrations or manual data scraping, enabling developers to focus on building higher‑level logic rather than handling low‑level API plumbing.

The server’s core value lies in its ability to provide real‑time infrastructure context. With endpoints such as , , and , an LLM can retrieve up‑to‑date statistics—CPU load, memory usage, storage health, and VM state—without leaving the conversational flow. For example, a developer could ask an assistant, “Which VMs are currently using more than 80 % CPU on node ?” and receive an immediate, structured answer that the assistant can use to generate troubleshooting steps or automated remediation scripts.

Key capabilities include:

• Cluster‑wide visibility: Obtain high‑level summaries of the entire Proxmox environment, useful for monitoring dashboards or incident triage.
• Node‑specific queries: Access detailed metrics and configuration for individual nodes, enabling targeted diagnostics.
• VM introspection: Retrieve status, resource allocation, and configuration of any virtual machine, facilitating lifecycle management or compliance checks.
• Listing utilities: Enumerate all VMs in the cluster to support inventory or capacity planning tasks.
• Docker & Docker Compose deployment: Simplify rollout in containerized environments, making the server portable across cloud or on‑premise setups.
• Secure credential handling: Store API credentials in environment variables, ensuring that sensitive information is not exposed in code or logs.

In real‑world scenarios, this MCP server powers automated support assistants that can suggest configuration changes, trigger snapshot backups, or alert operators to impending resource exhaustion—all without manual intervention. Developers integrating the server into their AI workflows can simply add a context provider in their MCP client, then reference the exposed endpoints directly within prompts or tool calls. The result is a seamless, declarative interface where AI agents can “understand” and act upon the state of their virtual infrastructure as naturally as they handle text.

Unique advantages stem from its tight coupling with Proxmox’s native API and the MCP abstraction layer. The server guarantees that context data is always consistent with the underlying system, reducing the risk of stale or incorrect information. Additionally, its lightweight Docker image and environment‑variable configuration make it ideal for rapid prototyping or production deployments in diverse environments, from private data centers to hybrid cloud setups.