qainsights/locust-mcp-server

A Model Context Protocol (MCP) server implementation for running Locust load tests. This server enables seamless integration of Locust load testing capabilities with AI-powered development environments.
✨ Features
- Simple integration with Model Context Protocol framework
- Support for headless and UI modes
- Configurable test parameters (users, spawn rate, runtime)
- Easy-to-use API for running Locust load tests
- Real-time test execution output
- HTTP/HTTPS protocol support out of the box
- Custom task scenarios support
🔧 Prerequisites
Before you begin, ensure you have the following installed:
- Python 3.13 or higher
- uv package manager (Installation guide)
📦 Installation
1. Clone the repository:
2. Install the required dependencies:
3. Set up environment variables (optional):
Create a .env file in the project root:
🚀 Getting Started
1. Create a Locust test script (e.g., hello.py):
2. Configure the MCP server using the below specs in your favorite MCP client (Claude Desktop, Cursor, Windsurf and more):
3. Now ask the LLM to run the test e.g. run locust test for hello.py. The Locust MCP server will use the following tool to start the test:
- run_locust: Run a test with configurable options for headless mode, host, runtime, users, and spawn rate
📝 API Reference
Run Locust Test
Parameters:
- test_file: Path to your Locust test script
- headless: Run in headless mode (True) or with UI (False)
- host: Target host to load test
- runtime: Test duration (e.g., "30s", "1m", "5m")
- users: Number of concurrent users to simulate
- spawn_rate: Rate at which users are spawned
✨ Use Cases
- LLM powered results analysis
- Effective debugging with the help of LLM
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.