Turns natural-language API descriptions into runnable k6 load tests, executes them, and returns structured performance metrics for AI-assisted reporting.
Enables LLM agents to run self-healing JMeter performance tests via natural language, from environment setup to capacity discovery and SLO verdict reporting.
A Model Context Protocol (MCP) server implementation that allows AI assistants to run k6 load tests through natural language commands, supporting custom test durations and virtual users.
Integrates Apache JMeter with AI assistants to run and manage load tests through natural language. It enables users to execute test plans, parse results, inspect test structures, and compare performance metrics across different runs.
Enables running and inspecting Artillery load tests from MCP-compatible clients like Claude Desktop and Cursor, with features like saved configurations, preset tests, and regression detection.
Enables AI assistants to programmatically create, execute, and analyze Apache JMeter performance tests. It supports automated bottleneck detection, report generation, and distributed testing management through natural language.