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Locust MCP Server

by QAInsights
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Python
Remote

🚀 ⚡️ 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

Related MCP server: JMeter 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.

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.

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TDQS

C2.7/5.0
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
ResponsivenessSyncing

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