$ loading_
Run and inspect Artillery load tests from MCP-compatible AI clients.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Artillery MCP Server" yet — see the docs or source repo.
Use the Artillery MCP Server to run a preset load test and return the test status, key metrics, and a summary of results.
Returns the load test result with status, main performance metrics, and a brief conclusion.
List the available saved Artillery configurations and describe what each one is suitable for testing.
Outputs a list of saved configurations with short descriptions of their intended use.
Compare the two most recent Artillery test results, identify any performance regression, and summarize abnormal metrics.
Provides a regression verdict and highlights performance metrics that may have worsened.
Developers or DevOps engineers can trigger Artillery load tests directly inside MCP-compatible clients such as Claude Desktop or Cursor without switching tools. This makes it faster to review test status and results.
When a team has standard load-testing routines, it can rerun them quickly through saved configurations or preset tests. This helps reduce variation caused by manual setup.
After a release or iteration, teams can use regression detection to compare test results and see whether performance has degraded. It is useful for pre- and post-release performance checks.
It is an MCP tool for running and inspecting Artillery load tests from MCP-compatible clients. Known features include saved configurations, preset tests, and regression detection.
The description mentions MCP-compatible clients such as Claude Desktop and Cursor. For the full compatibility scope, see the source repository.
The provided material does not include installation steps, runtime requirements, or API key prerequisites. See the source repository for details.
Run tests programmatically, inspect results, and get test strategy recommendations.
Upload Postman assets via MCP and run API tests with managed results.
Test all MCP protocol features when building and validating clients.
Transcribe audio, split recordings intelligently, and analyze meetings in MCP clients.
Build, debug, and manage software tasks with natural language across LLMs.
Generate production-ready MCP servers from databases or OpenAPI specs quickly.