Does my Jenkins data leave my environment?
No, all data processing happens locally; sensitive data is replaced with secure hashes before any AI communication and never leaves your environment.
@heniv96
AI-powered Jenkins pipeline intelligence platform with natural language interface. Provides comprehensive pipeline analysis, failure prediction, optimization suggestions, and automated Jenkinsfile reconstruction using Model Context Protocol (MCP) integration.
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": {
"mcp-jenkins-intelligence": {
"command": "python",
"args": [
"-m",
"pytest"
]
}
}
}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。
Mcp Jenkins Intelligence is a Model Context Protocol server that provides natural language interfaces for Jenkins pipeline operations, enabling professional DevOps teams to monitor, analyze, and optimize CI/CD workflows through AI-powered conversations in VS Code and Cursor. It integrates with the Jenkins API and uses FastMCP for communication.
Download a binary release from the GitHub releases page (or use the installer script) for your platform. Add a configuration entry to your MCP client (e.g., Cursor/VS Code) with the binary path and environment variables JENKINS_URL, JENKINS_USERNAME, and JENKINS_TOKEN.
No, all data processing happens locally; sensitive data is replaced with secure hashes before any AI communication and never leaves your environment.
You need a Jenkins instance with API access and credentials; the server requires the `JENKINS_URL`, `JENKINS_USERNAME`, and `JENKINS_TOKEN` environment variables.
Download the appropriate binary for your platform (macOS, Linux, Windows) from the GitHub releases page; no Python installation or additional dependencies are needed.
Yes, it supports standard Jenkins authentication as well as Azure AD integration for enterprise environments.
It offers natural language processing for conversational queries, anomaly detection, intelligent root cause analysis for failures, and automated suggestions for optimization.

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