HAProxy MCP Server

The HAProxy MCP Server bridges the gap between large language models and the real‑world configuration of a load balancer. By exposing HAProxy’s runtime API through the Model Context Protocol, developers can let an LLM issue commands such as adding or removing backend servers, adjusting weightings, or querying real‑time statistics—all in natural language. This eliminates the need for manual SSH sessions or writing ad‑hoc scripts, accelerating troubleshooting and dynamic scaling workflows.

At its core, the server implements full support for HAProxy’s runtime API. Every command that an administrator can execute via the native or interface is available as a structured MCP operation. The implementation handles context‑aware timeouts, cancellation, and error propagation so that the assistant can gracefully recover from network hiccups or malformed requests. In addition, the server integrates with HAProxy’s web‑based statistics page, providing richer metrics and visualizations that can be surfaced directly in the conversational UI.

Key capabilities include:

• Multiple transport layers – both stdio and HTTP transports are supported, allowing the server to run as a lightweight sidecar or a standalone service in diverse environments.
• Secure connections – TLS support for the runtime API ensures that sensitive commands are transmitted safely, a must‑have for production deployments.
• Docker ready – pre‑built images simplify deployment in container orchestration platforms, while the binary distribution keeps it lightweight for on‑premise use.
• Enterprise‑grade stability – the codebase is written in Go, leveraging mcp-go for robust MCP handling and includes comprehensive CI/CD pipelines to guarantee reliability.

Typical use cases span from automated load‑balancing during canary releases, to real‑time traffic analysis for security teams. For example, an AI assistant could answer a query like “Show me the current health of all backend servers” by invoking the stats API, then suggest adding a new node if latency thresholds are breached. In a DevOps setting, the same assistant can generate configuration snippets for new services and push them to HAProxy on demand.

By integrating this MCP server into an AI workflow, teams gain a conversational interface to their load balancer that is both powerful and secure. It turns static configuration files into dynamic, queryable services—enabling faster iteration, lower operational overhead, and a more intuitive way to manage traffic at scale.