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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.

    9 stars
    Python
    Updated Sep 19, 2025
    locust
    mcp
    mcp-server
    model-context-protocol
    performance-testing

    Table of Contents

    • ✨ Features
    • 🔧 Prerequisites
    • 📦 Installation
    • 🚀 Getting Started
    • 📝 API Reference
    • Run Locust Test
    • ✨ Use Cases
    • 🤝 Contributing
    • 📄 License

    Table of Contents

    • ✨ Features
    • 🔧 Prerequisites
    • 📦 Installation
    • 🚀 Getting Started
    • 📝 API Reference
    • Run Locust Test
    • ✨ Use Cases
    • 🤝 Contributing
    • 📄 License

    Documentation

    🚀 ⚡️ 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:

    • Python 3.13 or higher
    • uv package manager (Installation guide)

    📦 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.

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