iflow-mcp_qainsights-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 (Clau…