JMeter MCP Server

What is JMeter MCP Server about?

JMeter MCP Server enables remote execution of JMeter test plans in both GUI and non‑GUI modes via MCP‑compatible tools. It captures execution logs, builds the standard JMeter report dashboard, parses JTL files, computes detailed performance statistics, identifies bottlenecks, and produces visualizations and HTML reports.

How to use JMeter MCP Server?

1. Install dependencies – ensure JMeter is installed and reachable from the command line, install uv (or use a Python environment), and add numpy and matplotlib.
2. Configure environment – create a .env file with JMETER_HOME and optionally JMETER_JAVA_OPTS.
3. Start the server – run the Python entry point (e.g., python jmeter_server.py or via uv run jmeter_server.py).
4. Connect with an MCP client – add the server definition to the client’s mcpServers JSON (see README), then send prompts such as Run JMeter test /path/to/test.jmx.
5. Invoke tools – the client can call execute_jmeter_test_non_gui, analyze_jmeter_results, identify_performance_bottlenecks, get_performance_insights, and generate_visualization.

Key Features

• JMeter Execution: non‑GUI run for speed, optional GUI launch, real‑time output capture, automatic report dashboard generation.
• Result Parsing: supports XML and CSV JTL formats, streaming parser for large files.
• Metrics Calculation: averages, percentiles, endpoint‑specific stats, time‑series data.
• Bottleneck Detection: slow endpoints, high error rates, response‑time anomalies, concurrency impact.
• Insights & Recommendations: actionable suggestions, scaling guidance, prioritized fixes.
• Visualizations: time‑series graphs, distribution histograms, endpoint comparison charts, full HTML report.
• Error Handling: file existence checks, extension validation, graceful error propagation.

Use Cases

• Automated Load Testing: integrate with CI/CD pipelines to run JMeter tests on demand and retrieve structured results.
• Performance Debugging: developers ask the server to pinpoint slow services and receive concrete improvement advice.
• Stakeholder Reporting: generate ready‑to‑share HTML dashboards with metrics, charts, and executive summaries.
• Interactive Exploration: analysts use MCP‑compatible AI assistants (Claude Desktop, Cursor, Windsurf) to query results in natural language.

FAQ

Q: Do I need a GUI for test execution? A: GUI mode is optional and intended for test development; non‑GUI mode is the default for performance.

Q: Which JTL formats are supported? A: Both XML and CSV JTL files are parsed.

Q: How are recommendations generated? A: The Insights Generator compares observed metrics against common performance patterns and provides prioritized actions.

Q: Can I customize the report layout? A: The server outputs a standard JMeter HTML dashboard; additional customization requires modifying the visualization engine.

Q: What MCP clients are compatible? A: Any client that follows the Model Context Protocol, such as Claude Desktop, Cursor, or Windsurf.

🚀 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