k6 MCP Server

Overview

The k6‑mcp-server is a lightweight Model Context Protocol (MCP) implementation that bridges AI assistants with the popular k6 load‑testing tool. It allows conversational agents such as Claude, Cursor, or Windsurf to issue natural language commands that trigger k6 executions directly from the chat interface. By exposing a small set of MCP resources, the server turns ordinary text prompts into fully‑managed load tests without requiring users to leave their AI workflow or manually invoke command‑line tools.

What problem does it solve?

Developers and QA engineers often need to run quick performance checks while iterating on code or discussing results with stakeholders. Traditionally this requires context switching between a terminal, a test script editor, and an AI chat window. The k6‑mcp-server eliminates that friction: a single “run k6 test” prompt in the chat launches a load test, streams real‑time output back to the assistant, and even accepts custom duration or virtual user (VU) counts. This unified experience speeds up testing cycles, reduces errors from manual command construction, and keeps the conversation focused on insights rather than tooling.

Core capabilities

• MCP‑compatible command interface – The server registers two callable tools: (default 30 s, 10 VUs) and (user‑specified duration/VU).
• Real‑time feedback – Test progress and console output are streamed back to the client as they occur, enabling live monitoring within the chat.
• Environment‑driven configuration – Path to the k6 binary, test directory, and other parameters can be set via environment variables or a file, keeping deployment flexible.
• Zero‑code integration – Clients only need to add a small MCP spec pointing to the server executable; no additional code is required in the AI platform.

Use cases and scenarios

• Rapid performance prototyping – A developer can ask the assistant to “run k6 test for hello.js” while editing the script, instantly receiving results.
• Continuous integration feedback – CI pipelines can invoke the MCP server through an AI assistant to surface performance regressions in natural language reports.
• Collaborative debugging – Teams can discuss load‑test metrics within the chat, while the assistant runs new tests on demand, facilitating faster root‑cause analysis.
• Educational tooling – Students learning load testing can experiment with k6 via conversational prompts, receiving immediate visual and textual feedback.

Integration into AI workflows

The server fits seamlessly into any MCP‑capable client. Once the MCP spec is added, the assistant can interpret user intent (e.g., “run a 2‑minute test with 50 VUs”) and translate it into the appropriate tool call. The response, containing live output or a final summary, is returned as part of the chat context, allowing subsequent prompts to reference earlier results. This tight loop reduces context switching and keeps performance data close to the decision‑making conversation.

Unique advantages

• Minimal footprint – Written in Python, it requires only the k6 binary and the uv package manager, making it easy to deploy in containers or serverless environments.
• Extensibility – Developers can add more k6 options (e.g., thresholds, tags) by extending the existing tools without altering client code.
• Open‑source friendliness – Released under MIT, it encourages community contributions and rapid iteration on new features such as advanced reporting or integration with other monitoring stacks.

In summary, the k6‑mcp-server transforms a command‑line load‑testing tool into an AI‑friendly service, enabling developers and testers to orchestrate performance experiments directly from conversational interfaces while preserving real‑time visibility into test execution.