Locust MCP Server
🚀⚡️ locust-mcp-server
用于运行 Locust 负载测试的模型上下文协议 (MCP) 服务器实现。该服务器支持将 Locust 负载测试功能与 AI 驱动的开发环境无缝集成。
✨ 特点
与模型上下文协议框架的简单集成
支持无头和 UI 模式
可配置的测试参数(用户、生成率、运行时间)
用于运行 Locust 负载测试的易于使用的 API
实时测试执行输出
开箱即用的 HTTP/HTTPS 协议支持
自定义任务场景支持

Related MCP server: JMeter MCP Server
🔧 先决条件
开始之前,请确保已安装以下软件:
Python 3.13 或更高版本
uv 包管理器(安装指南)
📦安装
克隆存储库:
git clone https://github.com/qainsights/locust-mcp-server.git安装所需的依赖项:
uv pip install -r requirements.txt设置环境变量(可选):在项目根目录中创建一个
.env文件:
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🚀 入门
创建 Locust 测试脚本(例如
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"})在您最喜欢的 MCP 客户端(Claude Desktop、Cursor、Windsurf 等)中使用以下规格配置 MCP 服务器:
{
"mcpServers": {
"locust": {
"command": "/Users/naveenkumar/.local/bin/uv",
"args": [
"--directory",
"/Users/naveenkumar/Gits/locust-mcp-server",
"run",
"locust_server.py"
]
}
}
}现在让 LLM 运行测试,例如
run locust test for hello.py。Locust MCP 服务器将使用以下工具启动测试:
run_locust:使用可配置选项运行测试,包括无头模式、主机、运行时、用户和生成率
📝 API 参考
运行 Locust 测试
run_locust(
test_file: str,
headless: bool = True,
host: str = "http://localhost:8089",
runtime: str = "10s",
users: int = 3,
spawn_rate: int = 1
)参数:
test_file:Locust 测试脚本的路径headless:以无头模式运行(True)或使用 UI 运行(False)host:要进行负载测试的目标主机runtime:测试时长(例如“30秒”、“1分钟”、“5分钟”)users:要模拟的并发用户数spawn_rate:用户生成的速率
✨ 用例
LLM 支持的结果分析
在 LLM 的帮助下进行有效调试
🤝 贡献
欢迎贡献代码!欢迎提交 Pull 请求。
📄 许可证
该项目根据 MIT 许可证获得许可 - 有关详细信息,请参阅 LICENSE 文件。
Available Tools
1 toolrun_locustC
Run Locust with the given configuration.
Inputs: test_file (required), host, users, spawn_rate, +2 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'run_locust' has a clear and distinct purpose that cannot be mistaken for any other tool in this set.
The single tool name 'run_locust' follows a clear verb_noun pattern, and with only one tool, there is perfect consistency. There are no other tools to compare against, so no inconsistencies can exist.
A single tool is too few for most server purposes, as it severely limits functionality and flexibility. For a Locust server, one might expect additional tools for tasks like configuring tests, viewing results, or managing load scenarios, making this set feel incomplete and under-scoped.
The tool set is severely incomplete for a Locust server, which typically involves multiple aspects of load testing such as setup, execution, monitoring, and analysis. With only a 'run' tool, there are significant gaps that will cause agent failures, as it lacks coverage for configuration management, result retrieval, or test lifecycle operations.
Maintenance
Related MCP Connectors
MCP server for building and testing AI agents with multi-model experimentation and insights.
Load test your app on a real server from your agent. Get p95, errors and what ran out first.
MCP server that lets your AI assistant build and deploy apps, hosting each one at a live URL.
Load & browser performance testing — drive MaxoPerf from your AI agent with your API key.
Related MCP Servers
- AlicenseCqualityDmaintenanceA Model Context Protocol (MCP) server implementation that allows AI assistants to run k6 load tests through natural language commands, supporting custom test durations and virtual users.226MIT
- FlicenseBqualityCmaintenanceA Model Context Protocol server that allows AI assistants to execute and manage JMeter performance tests through natural language commands.674-
- AlicenseAqualityDmaintenanceA server that allows using natural language to automate test flows with Playwright, leveraging MidScene's AI agent capabilities to interact with web elements and perform assertions.12ISC
- AlicenseBqualityCmaintenanceAn AI-powered server that provides rapid debugging of server logs with actionable fixes in under 30 seconds, featuring real-time monitoring and root cause analysis through Google Gemini integration.721 npmMIT