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

# Optional: JMeter Java options
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 a JMeter test and generate a results file
Run JMeter test sample_test.jmx in non-GUI mode and save results to results.jtl

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

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

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

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