Locust MCP Server
An MCP server for running Locust load tests. Configure test parameters like host, users, and spawn rate via environment variables.
🚀 ⚡️ 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)
📦 Installation
- Clone the repository:
git clone https://github.com/qainsights/locust-mcp-server.git
- Install the required dependencies:
uv pip install -r requirements.txt
- Set up environment variables (optional):
Create a
.envfile 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
- 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"})
- 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"
]
}
}
}
- 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 scriptheadless: Run in headless mode (True) or with UI (False)host: Target host to load testruntime: Test duration (e.g., "30s", "1m", "5m")users: Number of concurrent users to simulatespawn_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.
Serveurs connexes
Scout Monitoring MCP
sponsorPut performance and error data directly in the hands of your AI assistant.
Alpha Vantage MCP Server
sponsorAccess financial market data: realtime & historical stock, ETF, options, forex, crypto, commodities, fundamentals, technical indicators, & more
MCP Server Template
A starter template for building a Model Context Protocol (MCP) server using TypeScript and Node.js.
MCP Server Executable
An executable server for running MCP services, featuring tool chaining, multi-service management, and plugin support.
Mermaid MCP Server
Converts Mermaid diagrams to PNG or SVG images.
Inoyu Apache Unomi
Maintains user context and manages profiles using the Apache Unomi Customer Data Platform.
ServiceNow
A production-ready Model Context Protocol (MCP) server for ServiceNow platform integration. Built with TypeScript for Node.js 20+, this server enables LLMs and AI assistants to interact with ServiceNow instances through a standardized interface.
Crypto HFT MCP Server
Integrate AI with high-frequency cryptocurrency trading systems.
Gemini CLI
Integrates with the unofficial Google Gemini CLI, allowing file access within configured directories.
Arch Tools
53 production-ready AI tools via MCP with x402 USDC payments on Base L2 — web scraping, crypto data, AI generation, OCR, and more.
QGIS
connects QGIS Desktop to Claude AI through the MCP. This integration enables prompt-assisted project creation, layer loading, code execution, and more.
Remote MCP Server (Authless)
An example of a remote MCP server deployable on Cloudflare Workers without authentication.