What are the prerequisites?
JMeter must be installed and accessible on the command line. You also need `uv` and Python dependencies (`numpy`, `matplotlib`).
@QAInsights
基本情報
✨ JMeter Meets AI Workflows: Introducing the JMeter MCP Server! 🤯
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"jmeter": {
"command": "/path/to/uv",
"args": [
"--directory",
"/your/dir/Gits/jmeter-mcp-server",
"run",
"jmeter_server.py"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
It is a Model Context Protocol (MCP) server that lets MCP-compatible clients (e.g., Claude Desktop, Cursor, Windsurf) execute JMeter tests and analyze results. It is designed for performance testers and developers who want to run and interpret JMeter workloads through natural language prompts.
Install uv, ensure JMeter is on the system and executable, then install Python dependencies (numpy, matplotlib). Configure the .env file with JMETER_HOME and JMETER_BIN. Connect via an MCP client and use tools like execute_jmeter_test_non_gui, analyze_jmeter_results, identify_performance_bottlenecks, get_performance_insights, and generate_visualization.
JMeter must be installed and accessible on the command line. You also need `uv` and Python dependencies (`numpy`, `matplotlib`).
Set `JMETER_HOME` and `JMETER_BIN` in the `.env` file. Optional `JMETER_JAVA_OPTS` can be added for Java heap settings.
Execution tools: `execute_jmeter_test` (GUI) and `execute_jmeter_test_non_gui`. Analysis tools: `analyze_jmeter_results`, `identify_performance_bottlenecks`, `get_performance_insights`, `generate_visualization`.
Yes, the `execute_jmeter_test` tool launches JMeter in GUI mode, but it does not run the test – that matches standard JMeter behavior.
Both XML and CSV JTL formats are supported. The parser uses streaming parsers for efficient processing of large files.
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Model Context Protocol Servers
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