README.md•3.39 kB
# 🚀 ⚡️ 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.