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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.

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🚀 ⚡️ 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

Locust-MCP-Server

🔧 Prerequisites

Before you begin, ensure you have the following installed:

📦 Installation

  1. Clone the repository:
git clone https://github.com/qainsights/locust-mcp-server.git
  1. Install the required dependencies:
uv pip install -r requirements.txt
  1. Set up environment variables (optional): Create a .env file in the project root:
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):
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"})
  1. Configure the MCP server using the below specs in your favorite MCP client (Claude Desktop, Cursor, Windsurf and more):
{
  "mcpServers": {
    "locust": {
      "command": "/Users/naveenkumar/.local/bin/uv",
      "args": [
        "--directory",
        "/Users/naveenkumar/Gits/locust-mcp-server",
        "run",
        "locust_server.py"
      ]
    }
  }
}
  1. 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

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.

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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.

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