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2025-04-07

用于运行蝗虫负载测试的模型上下文协议(MCP)服务器实现。该服务器可以通过AI驱动的开发环境将蝗虫负载测试功能无缝集成。

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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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    Reviews

    4 (1)
    Avatar
    user_QER0WheO
    2025-04-17

    I've been using the locust-mcp-server developed by QAInsights and it has significantly improved my load testing workflow. Its seamless integration with Locust and easy-to-navigate interface are commendable. It streamlines the process of scaling Locust workers and managing multiple load test scenarios efficiently. Highly recommended for anyone serious about load testing! Check it out here: https://github.com/QAInsights/locust-mcp-server