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heniv96MCP SERVER

MCP Jenkins Intelligence

Ask questions about Jenkins pipelines in natural language — health, failures, trends and dependencies — with the sensitive data anonymised before it is analysed.

by heniv96 ↗Build Systems & CI/CDCommunity

LABSCO SUMMARY

The anonymisation is what makes it usable on a real estate.

CI metadata is more sensitive than it looks — pipeline and repository names describe a company's internal structure, and log lines carry tokens. Hashing that before analysis is what lets you point this at a production Jenkins rather than a sandbox. The queue monitor and the dependency view are the tools that pay for themselves fastest when a build is stuck.

What it is

An MCP server for Jenkins that goes past listing builds. Alongside the read tools it carries analysis: pipeline health over a period, failure diagnosis for a specific build, trend comparison across pipelines, anomaly detection and optimisation suggestions. Processing happens locally, with pipeline names, branches, repositories and other identifying data replaced by hashes before analysis.

What you get

  • Read the pipelines: list_pipelines with an optional search, get_pipeline_details and get_pipeline_builds filtered by limit and status
  • Diagnose: analyze_pipeline_health over a period, analyze_pipeline_failure for one build_number, and ask_pipeline_question for a natural-language question across named pipelines
  • Monitor: get_pipeline_metrics, get_pipeline_dependencies, monitor_pipeline_queue for the Jenkins build queue and analyze_build_trends across several pipelines over a period
  • Look ahead: predict_pipeline_failure, detect_pipeline_anomalies with an adjustable sensitivity, suggest_pipeline_optimization and generate_ai_insights by insight type
  • Anonymisation that covers pipeline, cluster, folder, application, branch, organisation and repository names, along with build numbers, timestamps, URLs, user information, token values and log entries — applied recursively through nested structures

Requirements

A Jenkins server with API access and a token: JENKINS_URL, JENKINS_USERNAME and JENKINS_TOKEN are all required. No Python installation is needed for the released binaries — download the macOS arm64 or Linux amd64 build, make it executable, and point your client at its path with the three variables in the env block. Python 3.8 or higher is only needed for the development setup. All processing is local, with no data sent to external AI services.

Setup effort

One command plus a key — curl -fsSL https://raw.githubusercontent.com/heniv96/mcp-jenkins-intelligence/main/install.sh | bash, then supply credentials

See installation options