jmeter-mcp-server

jmeter-mcp-server

MCP server for generating, validating, running, and reporting Apache JMeter API performance tests from structured API sources.

Category
Visit Server

README

JMeter MCP Server

An MCP server for generating, validating, running, and reporting Apache JMeter API performance tests from structured API sources.

The project is designed for SDETs and automation engineers who want LLM-assisted test-plan creation without letting the LLM write arbitrary JMX. The LLM supplies structured MCP tool arguments; TypeScript code validates the input, applies deterministic load policies, renders JMX, and validates the generated file.

Features

  • Generate JMeter .jmx plans from OpenAPI files or explicit endpoint details.
  • Deterministic test profiles: smoke, baseline, load, spike, stress, soak, and custom.
  • Zod-validated MCP tool inputs.
  • Guardrails for aggressive load, documented rate limits, and profile mismatch.
  • Runtime parameterization for credentials with JMeter properties such as ${__P(API_KEY,)}.
  • OS-agnostic JMeter binary resolution through JMETER_BIN, JMETER_HOME, or jmeter on PATH.
  • TypeScript runner scripts for individual JMX files, suites, and JTL summary reports.
  • GitHub Actions CI for strict TypeScript build and guardrail tests.

Prerequisites

  • Node.js >=22
  • npm
  • Apache JMeter 5.6.x or compatible
  • Java supported by your JMeter installation

JMeter can be discovered in one of three ways:

export JMETER_BIN=/absolute/path/to/jmeter

or:

export JMETER_HOME=/absolute/path/to/apache-jmeter

or by having jmeter available on PATH.

Install

npm ci
npm run build:all
npm test

MCP Usage

Build the server:

npm run build

Configure your MCP client to run:

node /absolute/path/to/jmeter-mcp-server/build/index.js

Or use a short command after linking the package locally:

npm link
jmeter-mcp-server

See docs/MCP_CLIENTS.md for Claude, Claude Code, VS Code/Copilot-style, Continue-style, and generic MCP client examples.

Use the high-level tool for deterministic generation:

create_test_plan_from_api_source

Prefer this high-level tool over step-by-step JMX edits. It validates inputs, applies load-profile policy, renders a complete plan, and validates the generated JMX.

Test Profiles

Profile Users Ramp-up Loops Duration Notes
smoke 1 1s 1 none Minimal validation
baseline 5 5s 1 none Safe default
load 10 30s 3 none Moderate load
spike 25 1s 1 none Requires allowAggressiveLoad=true
stress 50 60s 5 none Requires allowAggressiveLoad=true
soak 5 60s forever 1800s Requires allowAggressiveLoad=true
custom required optional optional optional Fully explicit

Deterministic profiles reject mismatched overrides. For example, testProfile=baseline with users=6 fails. Use testProfile=custom when you need custom values.

Guardrails

The generation path is intentionally constrained:

LLM request
  -> MCP tool schema
  -> Zod validation
  -> deterministic profile policy
  -> source parsing
  -> normalized test plan model
  -> JMX renderer
  -> generated JMX validation
  -> output file

The MCP rejects:

  • missing users for custom profiles
  • deterministic profile overrides that do not match policy
  • aggressive profiles without allowAggressiveLoad=true
  • estimated request rates above a documented OpenAPI rate limit unless explicitly allowed
  • malformed headers, placeholder hosts, unsupported protocols, and incomplete request body metadata

Generated JMX validation checks required JMeter components, thread settings, loop settings, duration settings, endpoint target, headers, variables, assertions, and result collectors.

Credentials And Variables

Do not commit .env.

Copy the example locally:

cp .env.example .env

Generated JMX files use JMeter properties and do not need secrets embedded:

${__P(API_KEY,)}
${__P(OAUTH_CLIENT_ID,)}
${__P(OAUTH_CLIENT_SECRET,)}

At runtime, pass values through JMeter properties or set JMETER_ENV_FILE:

JMETER_ENV_FILE=.env npm run jmeter:run -- jmeter/API_Test_Plan_4th_API.jmx results/local-run

Scripts

Build:

npm run build:all

Run guardrail tests:

npm test

Generate JMX suite from OpenAPI:

npm run generate:jmeter:suite -- openapi.yaml jmeter 5

Run one JMX:

npm run jmeter:run -- jmeter/API_Test_Plan_1st_API.jmx results/local-run

Run a suite:

npm run jmeter:suite -- jmeter results/suite

Generate a suite report from JTL files:

npm run jmeter:report -- results/suite/<run-directory>

Rate Limits And Public APIs

Do not run aggressive tests against public or third-party APIs unless the API owner explicitly allows it.

For public practice APIs, prefer smoke or baseline. Profiles such as spike, stress, and soak require explicit opt-in through allowAggressiveLoad=true.

Repository Hygiene

Ignored by default:

  • .env, .env.*
  • .continue/, .vscode/
  • node_modules/
  • build/
  • results/
  • *.log
  • *.jtl

Commit-worthy files are source, configuration, docs, workflows, OpenAPI examples, and intentional JMX examples.

Development

npm ci
npm run build:all
npm test

CI runs:

npm run ci

which performs a strict TypeScript build and guardrail tests.

Status

This project is suitable for early public adoption by SDETs who are comfortable with MCP, JMeter, and TypeScript. The core guardrails are deterministic and tested. Additional API source adapters such as Postman collections, HAR files, or GraphQL schemas can be added behind the existing adapter interfaces.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured