locust-mcp-server

# 🚀 ⚡️ 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](https://github.com/astral-sh/uv)) ## 📦 Installation 1. Clone the repository: ```bash git clone https://github.com/qainsights/locust-mcp-server.git ``` 2. Install the required dependencies: ```bash uv pip install -r requirements.txt ``` 3. Set up environment variables (optional): Create a `.env` file in the project root: ```bash LOCUST_HOST=http://localhost:8089 # Default host for your tests LOCUST_USERS=3 # Default number of users LOCUST_SPAWN_RATE=1 # Default user spawn rate LOCUST_RUN_TIME=10s # Default test duration ``` ## 🚀 Getting Started 1. Create a Locust test script (e.g., `hello.py`): ```python from locust import HttpUser, task, between class QuickstartUser(HttpUser): wait_time = between(1, 5) @task def hello_world(self): self.client.get("/hello") self.client.get("/world") @task(3) def view_items(self): for item_id in range(10): self.client.get(f"/item?id={item_id}", name="/item") time.sleep(1) def on_start(self): self.client.post("/login", json={"username":"foo", "password":"bar"}) ``` 2. Configure the MCP server using the below specs in your favorite MCP client (Claude Desktop, Cursor, Windsurf and more): ```json { "mcpServers": { "locust": { "command": "/Users/naveenkumar/.local/bin/uv", "args": [ "--directory", "/Users/naveenkumar/Gits/locust-mcp-server", "run", "locust_server.py" ] } } } ``` 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 ```python run_locust( test_file: str, headless: bool = True, host: str = "http://localhost:8089", runtime: str = "10s", users: int = 3, spawn_rate: int = 1 ) ``` 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.