jmeter-mcp-server

This is a Model Context Protocol (MCP) server that allows executing JMeter tests through MCP-compatible clients and analyzing test results.

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📋 Features

JMeter Execution

• 📊 Execute JMeter tests in non-GUI mode
• 🖥️ Launch JMeter in GUI mode
• 📝 Capture and return execution output
• 📊 Generate JMeter report dashboard

Test Results Analysis

• 📈 Parse and analyze JMeter test results (JTL files)
• 📊 Calculate comprehensive performance metrics
• 🔍 Identify performance bottlenecks automatically
• 💡 Generate actionable insights and recommendations
• 📊 Create visualizations of test results
• 📑 Generate HTML reports with analysis results

🛠️ Installation

Local Installation

1. Install uv:
2. Ensure JMeter is installed on your system and accessible via the command line.

⚠️ Important: Make sure JMeter is executable. You can do this by running:

chmod +x /path/to/jmeter/bin/jmeter

3. Install required Python dependencies:

pip install numpy matplotlib

4. Configure the .env file, refer to the .env.example file for details.

JMETER_HOME=/path/to/apache-jmeter-5.6.3
JMETER_BIN=${JMETER_HOME}/bin/jmeter

JMETER_JAVA_OPTS="-Xms1g -Xmx2g"

💻 MCP Usage

1. Connect to the server using an MCP-compatible client (e.g., Claude Desktop, Cursor, Windsurf)
2. Send a prompt to the server:

Run JMeter test /path/to/test.jmx

3. MCP compatible client will use the available tools:

JMeter Execution Tools

• 🖥️ execute_jmeter_test: Launches JMeter in GUI mode, but doesn't execute test as per the JMeter design
• 🚀 execute_jmeter_test_non_gui: Execute a JMeter test in non-GUI mode (default mode for better performance)

Test Results Analysis Tools

• 📊 analyze_jmeter_results: Analyze JMeter test results and provide a summary of key metrics and insights
• 🔍 identify_performance_bottlenecks: Identify performance bottlenecks in JMeter test results
• 💡 get_performance_insights: Get insights and recommendations for improving performance
• 📈 generate_visualization: Generate visualizations of JMeter test results

🏗️ MCP Configuration

Add the following configuration to your MCP client config:

{
    "mcpServers": {
      "jmeter": {
        "command": "/path/to/uv",
        "args": [
          "--directory",
          "/path/to/jmeter-mcp-server",
          "run",
          "jmeter_server.py"
        ]
      }
    }
}

✨ Use Cases

Test Execution

• Run JMeter tests in non-GUI mode for better performance
• Launch JMeter in GUI mode for test development
• Generate JMeter report dashboards

Test Results Analysis

• Analyze JTL files to understand performance characteristics
• Identify performance bottlenecks and their severity
• Get actionable recommendations for performance improvements
• Generate visualizations for better understanding of results
• Create comprehensive HTML reports for sharing with stakeholders

🛑 Error Handling

The server will:

• Validate that the test file exists
• Check that the file has a .jmx extension
• Validate that JTL files exist and have valid formats
• Capture and return any execution or analysis errors

📊 Test Results Analyzer

The Test Results Analyzer is a powerful feature that helps you understand your JMeter test results better. It consists of several components:

Parser Module

• Supports both XML and CSV JTL formats
• Efficiently processes large files with streaming parsers
• Validates file formats and handles errors gracefully

Metrics Calculator

• Calculates overall performance metrics (average, median, percentiles)
• Provides endpoint-specific metrics for detailed analysis
• Generates time series metrics to track performance over time
• Compares metrics with benchmarks for context

Bottleneck Analyzer

• Identifies slow endpoints based on response times
• Detects error-prone endpoints with high error rates
• Finds response time anomalies and outliers
• Analyzes the impact of concurrency on performance

Insights Generator

• Provides specific recommendations for addressing bottlenecks
• Analyzes error patterns and suggests solutions
• Generates insights on scaling behavior and capacity limits
• Prioritizes recommendations based on potential impact

Visualization Engine

• Creates time series graphs showing performance over time
• Generates distribution graphs for response time analysis
• Produces endpoint comparison charts for identifying issues
• Creates comprehensive HTML reports with all analysis results

📝 Example Usage

Run JMeter test sample_test.jmx in non-GUI mode and save results to results.jtl

Analyze the JMeter test results in results.jtl and provide detailed insights

What are the performance bottlenecks in the results.jtl file?

What recommendations do you have for improving performance based on results.jtl?

Create a time series graph of response times from results.jtl