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JMeter MCP Server

JMeter MCP Server

by ebadAhmed10

🚀 JMeter MCP Server

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

IMPORTANT

📢 Looking for an AI Assistant inside JMeter? 🚀 Check outFeather Wand

Anthropic Cursor Windsurf

📋 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

Related MCP server: BlazeMeter MCP Server

🛠️ 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
  1. Install dependencies and run the server:

uv sync
  1. 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
  1. 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": "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

Available Tools

6 tools
analyze_jmeter_resultsC

Analyze JMeter test results and provide a summary of key metrics and insights.

Args: jtl_file: Path to the JTL file containing test results detailed: Whether to include detailed analysis (default: False)

Returns: str: Analysis results in a formatted string

Inputs: jtl_file (required), detailed. Returns: result

execute_jmeter_testB

Execute a JMeter test.

Args: test_file: Path to the JMeter test file (.jmx) gui_mode: Whether to run in GUI mode (default: False) properties: Dictionary of JMeter properties to pass with -J (default: None)

Inputs: test_file (required), gui_mode, properties. Returns: result

execute_jmeter_test_non_guiB

Execute a JMeter test in non-GUI mode - supports JMeter properties.

Args: test_file: Path to the JMeter test file (.jmx) properties: Dictionary of JMeter properties to pass with -J (default: None) generate_report: Whether to generate report dashboard after load test (default: False) report_output_dir: Output folder for report dashboard (default: None) log_file: Name of JTL file to log sample results to (default: None)

Inputs: test_file (required), properties, generate_report, report_output_dir, +1 more. Returns: result

generate_visualizationC

Generate visualizations of JMeter test results.

Args: jtl_file: Path to the JTL file containing test results visualization_type: Type of visualization to generate (time_series, distribution, comparison, html_report) output_file: Path to save the visualization

Returns: str: Path to the generated visualization file

Inputs: jtl_file (required), visualization_type (required), output_file (required). Returns: result

get_performance_insightsC

Get insights and recommendations for improving performance based on JMeter test results.

Args: jtl_file: Path to the JTL file containing test results

Returns: str: Performance insights and recommendations in a formatted string

Inputs: jtl_file (required). Returns: result

identify_performance_bottlenecksB

Identify performance bottlenecks in JMeter test results.

Args: jtl_file: Path to the JTL file containing test results

Returns: str: Bottleneck analysis results in a formatted string

Inputs: jtl_file (required). Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.5/5.0

    Scored across 6 tools

    Disambiguation4/5

    Most tools have distinct purposes: analyzing results, executing tests, generating visualizations, and identifying bottlenecks. However, 'analyze_jmeter_results' and 'get_performance_insights' could be confused as both provide analysis from test results, though one focuses on metrics and the other on recommendations.

    Naming Consistency5/5

    All tool names follow a consistent snake_case pattern with clear verb_noun structures, such as 'execute_jmeter_test' and 'generate_visualization'. The naming is uniform and predictable across all six tools.

    Tool Count5/5

    With 6 tools, the server is well-scoped for JMeter testing, covering execution, analysis, visualization, and bottleneck identification. Each tool serves a clear purpose without being overly sparse or bloated.

    Completeness4/5

    The tool set covers key aspects of JMeter testing: execution (with GUI and non-GUI options), result analysis, visualization, and bottleneck insights. A minor gap is the lack of tools for test creation or editing, but core workflows are well-supported.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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