A Model Context Protocol (MCP) server implementation that lets LLM agents trigger and monitor k6 load-tests.
https://github.com/QAInsights/k6-mcp-serverStop context-switching between your AI assistant and terminal to run performance tests. The k6 MCP server bridges k6 load testing with AI agents, letting you trigger tests and analyze results through natural language commands.
You're building a feature, want to test its performance, and here's what happens: you switch to terminal, remember the k6 command syntax, run the test, wait for completion, then manually parse the output. Need to adjust VUs or duration? More command-line juggling. Want to compare results across runs? Copy-paste metrics into your notes.
This MCP server eliminates that friction entirely.
Natural Language Test Execution: Tell Claude, Cursor, or any MCP-compatible AI assistant to "run a load test on my API endpoint with 50 users for 2 minutes" and it handles the k6 execution automatically.
AI-Powered Results Analysis: Instead of staring at raw k6 output, your AI assistant can interpret results, identify bottlenecks, suggest optimizations, and even compare performance across different test runs.
Seamless Integration: Works with your existing k6 scripts - no rewrites needed. The server acts as a bridge between your AI tools and k6 CLI.
During Development: "Test my authentication endpoint with 100 concurrent users and tell me if response times are acceptable for production."
Performance Debugging: "Run this test script and analyze why my 95th percentile response time spiked compared to yesterday's run."
Load Testing Experiments: "Try different VU configurations on this endpoint and recommend the optimal setup for our expected traffic."
CI/CD Integration: Ask your AI assistant to interpret performance test results in pull requests and flag potential regressions.
Add this to your MCP client configuration:
{
"mcpServers": {
"k6": {
"command": "/path/to/bin/uv",
"args": [
"--directory", "/path/to/k6-mcp-server",
"run", "k6_server.py"
]
}
}
}
Then simply ask your AI assistant: "Run k6 test for my-script.js with 20 users for 30 seconds."
Performance testing shouldn't require mental overhead switching between tools and interpreting raw metrics. When your AI assistant can execute tests and provide contextual analysis, you spend more time optimizing performance and less time wrestling with tooling.
The k6 MCP server transforms load testing from a separate workflow into a natural part of your AI-assisted development process. Your performance insights become as accessible as asking a question.
Ready to make your load testing conversational? Clone the repo and connect it to your favorite AI assistant in under 5 minutes.