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QAInsights

Locust MCP Server

by QAInsights
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

🚀 ⚡️ servidor locust-mcp

Implementación de un servidor de Protocolo de Contexto de Modelo (MCP) para ejecutar pruebas de carga de Locust. Este servidor permite una integración fluida de las capacidades de pruebas de carga de Locust con entornos de desarrollo basados en IA.

✨ Características

  • Integración sencilla con el marco del Protocolo de Contexto de Modelo

  • Compatibilidad con modos sin cabeza y de interfaz de usuario

  • Parámetros de prueba configurables (usuarios, tasa de generación, tiempo de ejecución)

  • API fácil de usar para ejecutar pruebas de carga de Locust

  • Salida de ejecución de pruebas en tiempo real

  • Compatibilidad con el protocolo HTTP/HTTPS lista para usar

  • Compatibilidad con escenarios de tareas personalizados

Servidor Locust-MCP

Related MCP server: JMeter MCP Server

🔧 Requisitos previos

Antes de comenzar, asegúrese de tener instalado lo siguiente:

📦 Instalación

  1. Clonar el repositorio:

git clone https://github.com/qainsights/locust-mcp-server.git

  1. Instale las dependencias necesarias:

uv pip install -r requirements.txt

  1. Configurar variables de entorno (opcional): Cree un archivo .env en la raíz del proyecto:

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

🚀 Primeros pasos

  1. Cree un script de prueba de Locust (por ejemplo, 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 el servidor MCP utilizando las siguientes especificaciones en su cliente MCP favorito (Claude Desktop, Cursor, Windsurf y más):

{
  "mcpServers": {
    "locust": {
      "command": "/Users/naveenkumar/.local/bin/uv",
      "args": [
        "--directory",
        "/Users/naveenkumar/Gits/locust-mcp-server",
        "run",
        "locust_server.py"
      ]
    }
  }
}

  1. Ahora solicite al LLM que ejecute la prueba, por ejemplo, run locust test for hello.py . El servidor Locust MCP usará la siguiente herramienta para iniciar la prueba:

  • run_locust : ejecuta una prueba con opciones configurables para el modo sin cabeza, el host, el tiempo de ejecución, los usuarios y la tasa de generación

Referencia de API

Ejecutar la prueba de langosta

run_locust(
    test_file: str,
    headless: bool = True,
    host: str = "http://localhost:8089",
    runtime: str = "10s",
    users: int = 3,
    spawn_rate: int = 1
)

Parámetros:

  • test_file : Ruta a su script de prueba de Locust

  • headless : Ejecutar en modo sin cabeza (Verdadero) o con interfaz de usuario (Falso)

  • host : host de destino para la prueba de carga

  • runtime : Duración de la prueba (p. ej., "30 s", "1 m", "5 m")

  • users : Número de usuarios simultáneos a simular

  • spawn_rate : Frecuencia con la que se generan los usuarios

✨ Casos de uso

  • Análisis de resultados impulsados por LLM

  • Depuración eficaz con la ayuda de LLM

🤝 Contribuyendo

¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.

📄 Licencia

Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.

Available Tools

1 tool
run_locustC

Run Locust with the given configuration.

ParametersJSON Schema
NameRequiredDescriptionDefault
test_fileYes
hostNohttp://localhost:8089
usersNo
spawn_rateNo
runtimeNo30s
headlessNo

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'run' but doesn't clarify if this is a read-only operation, if it modifies state (e.g., starts a process), potential side effects (e.g., consuming resources), or expected outputs (e.g., test results). This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, straightforward sentence that is front-loaded and wastes no words. However, it's overly concise to the point of under-specification, which slightly reduces its effectiveness. Still, it's structurally sound with no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover what the tool does beyond a high-level action, leaving the agent unsure about execution details, results, or error handling. This inadequacy is notable for a tool with multiple configuration options.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but fails to do so. It doesn't explain any parameters beyond implying a 'configuration' exists. For example, it doesn't clarify what 'test_file' should contain, the meaning of 'users' or 'spawn_rate', or how 'runtime' is formatted. This leaves all 6 parameters poorly understood.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Run Locust with the given configuration' states the action ('Run') and target ('Locust'), but it's vague about what Locust is (a load testing tool) and what 'run' entails (e.g., executing tests). It doesn't distinguish from siblings, but since there are none, this is less critical. However, the purpose remains somewhat ambiguous without context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool, such as for performance testing scenarios, prerequisites (e.g., having Locust installed), or alternatives. With no sibling tools, differentiation isn't needed, but it still lacks any usage context, leaving the agent to infer from the tool name and parameters alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • First observedrun_locust
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.7/5.0

Scored across 1 tool

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness1/5

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

ActivityInactive
ResponsivenessUnresponsive

Resources

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