jenkins-mcp-enterprise
MCP ServerJordan-Jarvis/jenkins-mcp-enterprise
An enterprise-grade Model Context Protocol (MCP) server designed to integrate LLMs with Jenkins for build diagnosis and pipeline management.
29 12GPL-3.0Updated 2026-07-22
76
GreatOverview
This repository provides a production-ready Model Context Protocol (MCP) server that connects large language models with Jenkins CI/CD environments. It features advanced pipeline log streaming, nested sub-build discovery, and optional vector search capabilities via Qdrant for semantic log retrieval. The toolset includes automated error pattern extraction, detailed diagnostic configurations, and robust Docker deployment options.
Capabilities
- ▸Jenkins connection and session management
- ▸Pipeline build triggering and status monitoring
- ▸Massive console log retrieval and streaming
- ▸Advanced regex pattern configuration for failure diagnosis
- ▸Optional vector/semantic search using Qdrant
- ▸Integration testing via MCP inspector and pytest
Best for
Diagnosing Jenkins CI/CD build and test failures automatically using LLMs, Streaming, processing, and analyzing massive pipeline log files (10+ GB), Discovering and navigating complex, deeply nested pipeline hierarchies and sub-builds, Performing semantic vector search over historical build logs to find recurring errors
Works with
Claude CodeCursor
Evoa Score breakdown= Σ (score × weight)
76Task usefulness20%100 → +20.0
実タスクにどれだけ役立つか（機能の豊富さ・用途の明確さ）。 — AIによるcapabilities/use-cases解析
Code quality15%90 → +13.5
実装・指示の品質。 — AIによるSKILL.md/README解析
Maintenance15%100 → +15.0
リポジトリがどれだけ活発に保守されているか。 — GitHub 最終push日時の新しさ
Documentation12%44 → +5.3
ドキュメントの充実度・分かりやすさ。 — README/独自要約の情報量
Security15%50 → +7.5
危険・不審な挙動が無いか。 — AIによるセキュリティレビュー
Originality10%85 → +8.5
ありふれたラッパーではない独自性。 — AIによる独自性判定
Popularity8%43 → +3.4
コミュニティの採用度。 — GitHub Stars/Forks（対数スケール）
Compatibility5%50 → +2.5
対応AIエージェントの広さ。 — AIによる対応エージェント判定
Weighted total75.7 / 100
ライセンス不明/制限あり（Red）のSkillは総合スコアに0.85倍の補正を適用します。 ランキングはこのScoreのみで決まり、広告で変わりません。 算出方法の詳細 →
Security considerations
Requires sensitive Jenkins credentials and API tokens stored in configuration files or environment variables. Ensure proper secret management when deploying via Docker or local environments.
Categories
Summary and analysis are original content generated by AI Skills Rank. The skill's source text is not reproduced here — view it on the linked repository.