JMeter MCP Server

What does “JMeter” MCP Server do?

The JMeter MCP Server is a Model Context Protocol (MCP) server designed to bridge Apache JMeter with AI-driven workflows. It enables AI assistants and compatible clients to execute JMeter tests programmatically, analyze test results, and integrate performance testing directly into automated development pipelines. By exposing JMeter’s functionality as tools and resources, this server allows developers to automate load testing, retrieve reports, and interact with test artifacts seamlessly. The JMeter MCP Server facilitates enhanced workflows by supporting both GUI and non-GUI test executions, capturing outputs, and generating comprehensive performance dashboards, thereby streamlining performance engineering tasks within modern AI-enhanced development environments.

List of Prompts

No explicit prompt templates are documented in the repository.

List of Resources

• JMeter Report Dashboard
  Provides access to the generated JMeter report dashboard after test execution.
• Execution Output
  Returns the output log or results from running a JMeter test.
• Sample Test Plan
  Offers a sample JMeter .jmx test plan as a template or starting point.

List of Tools

• Execute JMeter Test (Non-GUI Mode)
  Runs a JMeter test in non-GUI mode, suitable for automation and CI/CD integrations.
• Launch JMeter (GUI Mode)
  Initiates the JMeter application in GUI mode for manual test creation or debugging.
• Generate JMeter Report
  Produces a JMeter report dashboard summarizing performance results.
• Analyze Test Results
  Parses and analyzes output logs or result files for insights.

Use Cases of this MCP Server

• Automated Performance Testing
  Integrate JMeter test execution into AI workflows and CI/CD pipelines for continuous load and performance testing.
• Performance Results Analysis
  Quickly analyze and retrieve actionable insights from JMeter test results directly via AI assistants.
• On-the-fly Test Execution
  Allow developers or AI agents to trigger ad-hoc JMeter tests for new services or endpoints.
• Report Generation for QA
  Automatically generate and distribute performance dashboards after each test cycle for quality assurance reviews.
• AI-Driven Test Orchestration
  Enable LLMs to coordinate complex testing scenarios, run batch tests, and manage JMeter configurations programmatically.

How to set it up

Windsurf

1. Ensure Python and JMeter are installed on your system.
2. Clone or download the jmeter-mcp-server repository.
3. Edit your Windsurf configuration file to add the JMeter MCP server.
4. Insert the following JSON snippet into the mcpServers section:

   {
     "jmeter-mcp": {
       "command": "python",
       "args": ["main.py"]
     }
   }
   

5. Save the configuration and restart Windsurf.
6. Verify the server is running and accessible from Windsurf.

Claude

1. Install prerequisites (Python, JMeter).
2. Download the JMeter MCP server and ensure main.py is executable.
3. Update your Claude tool configuration to include the MCP server.
4. Add to your config:

   {
     "jmeter-mcp": {
       "command": "python",
       "args": ["main.py"]
     }
   }
   

5. Restart Claude and check for MCP server integration.

Cursor

1. Set up Python and JMeter.
2. Download or clone the repository.
3. Access Cursor settings and locate the MCP server configuration.
4. Add:

   {
     "jmeter-mcp": {
       "command": "python",
       "args": ["main.py"]
     }
   }
   

5. Save and restart Cursor.

Cline

1. Install Python and JMeter.
2. Obtain the MCP server files and ensure Python dependencies are installed.
3. Edit the Cline configuration to register the MCP server:

   {
     "jmeter-mcp": {
       "command": "python",
       "args": ["main.py"]
     }
   }
   

4. Save and restart Cline.

Note on Securing API Keys:
Environment variables can be used to secure sensitive data like API keys. Example:

{
  "jmeter-mcp": {
    "command": "python",
    "args": ["main.py"],
    "env": {
      "JMETER_API_KEY": "${JMETER_API_KEY}"
    },
    "inputs": {
      "api_key": "${JMETER_API_KEY}"
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "jmeter-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “jmeter-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The JMeter MCP Server is well-suited for teams looking to automate performance testing and integrate JMeter into AI-powered workflows. The documentation covers features and setup for various platforms, though it lacks explicit prompt templates and detailed sampling/root support. Its tool and resource exposure is robust for performance engineering tasks.

MCP Score

Rating: 6/10
The server provides core MCP functionality and clear setup guidance but lacks documented prompt templates, LICENSE, and explicit sampling/roots support, which would make it more production-ready and open-source friendly.