https://github.com/qainsights/jmeter-mcp-server
✨ JMeter Meets AI Workflows: Introducing the JMeter MCP Server! 🤯
https://github.com/qainsights/jmeter-mcp-server
apache-jmeter jmeter mcp model-context-protocol-servers performance
Last synced: 4 months ago
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✨ JMeter Meets AI Workflows: Introducing the JMeter MCP Server! 🤯
- Host: GitHub
- URL: https://github.com/qainsights/jmeter-mcp-server
- Owner: QAInsights
- Created: 2025-03-28T14:43:51.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-06-15T14:06:26.000Z (5 months ago)
- Last Synced: 2025-06-15T15:13:10.648Z (5 months ago)
- Topics: apache-jmeter, jmeter, mcp, model-context-protocol-servers, performance
- Language: Python
- Homepage: https://jmeter.ai
- Size: 1.09 MB
- Stars: 27
- Watchers: 2
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 🚀 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 out [Feather Wand](https://jmeter.ai)
## 📋 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`](https://github.com/astral-sh/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:
```bash
chmod +x /path/to/jmeter/bin/jmeter
```
3. Install required Python dependencies:
```bash
pip install numpy matplotlib
```
4. Configure the `.env` file, refer to the `.env.example` file for details.
```bash
# 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:
```json
{
"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
```