Locust MCP Server

The Qainsights Locust MCP Server bridges the gap between traditional load‑testing tools and modern AI‑augmented development workflows. By exposing Locust’s powerful distributed testing engine through the Model Context Protocol (MCP), it allows AI assistants—such as Claude, Cursor, or Windsurf—to orchestrate performance tests directly from the chat interface. This eliminates the need for manual command‑line interaction and enables rapid, repeatable testing cycles that can be triggered by natural language commands or automated prompts.

At its core, the server provides a single, well‑defined tool: . Developers can specify a Locust test script and configure key parameters—headless or UI mode, target host, runtime duration, user count, and spawn rate—all in a concise JSON payload. The MCP server translates these inputs into a Locust execution, streams real‑time output back to the client, and gracefully handles completion or failure. This tight integration means that performance metrics can be discussed in context, annotated with AI insights, and fed into continuous‑integration pipelines without leaving the assistant environment.

Key capabilities include:

• Headless and UI support: Run tests entirely in the background or launch Locust’s web dashboard for interactive monitoring.
• Configurable test parameters: Adjust users, spawn rate, and runtime on the fly to model different traffic scenarios.
• Real‑time output streaming: Observe live metrics such as requests per second, response times, and failures directly in the assistant’s chat window.
• HTTP/HTTPS protocol handling: Test any web service out of the box, with full support for custom headers, authentication, and task scenarios.

Typical use cases span from quick sanity checks—“run a 30‑second test with 5 users on the staging API”—to complex load‑testing pipelines where an AI assistant triggers tests after code merges, analyzes results, and suggests bottleneck fixes. Because the server exposes a simple API, it can be embedded into CI/CD workflows, chat‑ops platforms, or even IDE extensions that support MCP.

What sets this implementation apart is its simplicity and flexibility. The server requires only Python 3.13+ and the uv package manager, yet it offers a rich set of features that make Locust accessible to developers who may not be familiar with its command‑line interface. By packaging load testing as an MCP service, it unlocks a new paradigm where AI assistants become first‑class orchestrators of performance engineering tasks.