mcp-jenkins-intelligence
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.
Install / Use
claude mcp add heniv96 -- npx -y github:heniv96/mcp-jenkins-intelligenceIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of mcp-jenkins-intelligence
mcp-jenkins-intelligence scores 78/100 on our quality scale, 324th of 647 Automation skills we index.
Its MCP Server is 22 KB long, well organised into 58 sections with 9 code examples: a thorough specification that gives an agent plenty to work with.
It has 3 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 12 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 86/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
mcp-jenkins-intelligence compared with similar skills
All 4 of these similar skills score higher than mcp-jenkins-intelligence; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| mcp-jenkins-intelligence (this skill)by heniv96 | 78 | 3 | 12mo ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 85.0k | 8d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.6k | today | CLAUDE.md |
| rufloby ruvnet | 100 | 73.1k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.1k | today | CLAUDE.md |
Frequently asked questions
- How do I install mcp-jenkins-intelligence?
- Run
claude mcp add heniv96 -- npx -y github:heniv96/mcp-jenkins-intelligence. The install tabs above show the steps for each supported agent. - Which AI agents does mcp-jenkins-intelligence work with?
- It is written for Claude Code, Claude Desktop and Cursor, as a MCP Server file. Other agents that read the same format can often use it too.
- Is mcp-jenkins-intelligence safe to use?
- It is MIT-licensed and scores 86/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is mcp-jenkins-intelligence still maintained?
- The repository was last updated about 12 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubMCP Jenkins Intelligence
The Jenkins Intelligence Platform
Transform your Jenkins operations with AI-powered natural language interfaces and comprehensive pipeline analysis.
Quick Start (Binary Distribution)
Prefer a ready-to-use binary? Download the latest release and start using MCP Jenkins Intelligence in seconds!
Download & Install
# Option 1: Use the installer script (recommended)
curl -fsSL https://raw.githubusercontent.com/heniv96/mcp-jenkins-intelligence/main/install.sh | bash
# Option 2: Manual download
# Download from: https://github.com/heniv96/mcp-jenkins-intelligence/releases/latest
# Choose the appropriate binary for your platform:
# - mcp-jenkins-server-macos-arm64 (macOS Apple Silicon)
# - mcp-jenkins-server-linux-amd64 (Linux AMD64)
# Make executable: chmod +x mcp-jenkins-server-<platform>
MCP Configuration
Add to your MCP client configuration (Cursor/VSCode):
{
"mcpServers": {
"mcp-jenkins-intelligence": {
"command": "/path/to/mcp-jenkins-server",
"args": [],
"env": {
"JENKINS_URL": "https://your-jenkins-url",
"JENKINS_USERNAME": "your-username",
"JENKINS_TOKEN": "your-token"
}
}
}
}
That's it! No Python installation, no dependencies - just download and run!
Overview
MCP Jenkins Intelligence is a comprehensive Model Context Protocol (MCP) solution designed for professional DevOps teams. It provides natural language interfaces for complex Jenkins pipeline operations, enabling teams to monitor, analyze, and optimize their CI/CD workflows through AI-powered conversations in VSCode and Cursor.
Key Features
Intelligent Pipeline Analysis
- Real-time Monitoring: Live pipeline status, health metrics, and performance analytics
- AI-Powered Insights: Natural language queries for complex pipeline analysis
- Failure Analysis: Deep dive into pipeline failures with intelligent root cause analysis
- Performance Optimization: Automated suggestions for improving build times and success rates
- Advanced Analytics: Comprehensive reporting and performance comparisons
- Anomaly Detection: AI-powered detection of unusual pipeline behavior patterns
Advanced AI Capabilities
- Natural Language Processing: Conversational interface for complex DevOps operations
- Smart Diagnostics: AI-driven pipeline health analysis and troubleshooting guidance
- Context-Aware Prompts: Intelligent prompt suggestions for different analysis scenarios
- Automated Reporting: Proactive identification of issues and optimization opportunities
Enterprise Security & Compliance
- Multi-Authentication Support: Standard Jenkins and Azure AD integration
- Secure Communication: TLS encryption for all Jenkins API communications
- Audit Logging: Comprehensive audit trails for all pipeline operations
- Minimal Privilege: Secure by design with least privilege access patterns
- Enterprise-Grade Data Protection: 19+ protection patterns for complete data anonymization
- Complete Anonymization: Pipeline names, cluster names, folder names, app names, branch names, organization names, repository names, and code file names are all protected
- Hash-based Security: Sensitive data is replaced with secure hashes before AI communication
- Local Execution: All data processing happens locally - no data leaves your environment
- Recursive Protection: Works on nested data structures and complex objects
- Access Control Auditing: Comprehensive permission and access control analysis
Advanced Analytics & Reporting
- Comprehensive Reports: Generate detailed reports with metrics and insights
- Performance Comparisons: Compare pipeline performance across teams and environments
- Trend Analysis: Long-term performance and reliability trend analysis
Advanced AI Features
- Anomaly Detection: AI-powered detection of unusual pipeline behavior patterns
- Comprehensive Insights: AI-generated insights and recommendations
Performance Optimization
- Build Time Analysis: Detailed analysis and optimization suggestions for build times
Deployment & Distribution
- Multiple Deployment Options: Development setup or production deployment
- Cross-Platform Support: Works on macOS, Linux, and Windows
- Easy Configuration: Simple setup with environment variables or MCP config
Architecture
MCP Protocol Integration
The following diagram shows how MCP Jenkins Intelligence integrates with VSCode and Cursor AI through the Model Context Protocol:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ VSCode/ │ │ MCP Protocol │ │ Jenkins │
│ Cursor AI │◄──►│ │◄──►│ Intelligence │
│ │ │ │ │ Server │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ Jenkins API │
└─────────────────────────────────────────┘
│
▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ AI Analysis │ │ Core Tools │ │ MCP Resources │
│ Engine │ │ (30 tools) │ │ & Prompts │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ • Health │ │ • List │ │ • Status │
│ Analysis │ │ • Details │ │ Resource │
│ • Failure │ │ • Builds │ │ • Summary │
│ Analysis │ │ • Configure │ │ Resource │
│ • AI Queries │ │ • Test │ │ • Dashboard │
│ • Metrics │ │ • Questions │ │ Resource │
│ • Dependencies │ │ • Trigger │ │ • Logs │
│ • Trends │ │ • Stop │ │ Resource │
│ • Security │ │ • Enable/Dis │ │ • Health │
│ • Export │ │ • Config │ │ Resource │
│ • Optimize │ │ • Predict │ │ • Analysis │
│ │ │ • Suggest │ │ Prompts │
└─────────────────┘ └─────────────────┘ └─────────────────┘
Modular Architecture
The internal architecture follows a clean, modular design with separation of concerns:
┌─────────────────────────────────────────────────────────────────────────┐
│ MCP Layer │
├─────────────────┬─────────────────┬─────────────────────────────────────┤
│ FastMCP Server │ Tool Registry │ Request Router │
└─────────────────┴─────────────────┴─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Modular Services │
├─────────────┬─────────────┬─────────────┬─────────────┬─────────────────┤
│ Models │ Services │ Resources │ Prompts │ │
├─────────────┼─────────────┼─────────────┼─────────────┼─────────────────┤
│ • Pipeline │ • Jenkins │ • Status │ • Analysis │ │
│ • Build │ • Core │ • Summary │ • Failure │ │
│ • Health │ • Control │ • Dashboard │ • Optimize │ │
│ • Failure │ • Monitor │ • Logs │ • Security │ │
│ • Query │ • AI │ • Health │ │ │
│ │ • Security │ │ │ │
└─────────────┴─────────────┴─────────────┴─────────────┴─────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Tool Categories │
├─────────────────┬─────────────────┬─────────────────┬─────────────────┤
│ Core Tools (9) │ Control Tools │ Monitoring (4) │ AI Tools (5) │
│ │ (4) │ │ │
├─────────────────┼─────────────────┼─────────────────┼─────────────────┤
│ • list_pipelines│ • trigger_build │ • get_metrics │ • Predict │
│ • get_details │ • stop_build │ • dependencies │ Failure │
│ • get_builds │ • enable_disable│ • monitor_queue │ • Suggest │
│ • ask_questions │ • get_config │ • analyze_trends│ Optimize │
│ • configure_ │ │ │ • Anomaly │
│ jenkins │ │ │ Detection │
│ • test_ │ │ │ • AI │
│ connection │ │ │ Insights │
│ • analyze_ │ │ │ • Retry │
│ health │ │ │ Logic │
│ • analyze_ │ │ │ │
│ failure │ │ │ │
│ • get_server_ │ │ │ │
│ info │ │ │ │
└─────────────────┴─────────────────┴─────────────────┴─────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Additional Tool Categories │
├─────────────┬─────────────┬─────────────┬─────────────────────────────┤
│ Security │ Jenkinsfile │ Analytics │ Performance │
│ (2) │ (3) │ (2) │ (1) │
├─────────────┼─────────────┼─────────────┼─────────────────────────────┤
│ • scan_ │ • get_ │ • generate_ │ • analyze_ │
│ security │ jenkinsfile│ report │ build_time │
│ │ • reconstruct│ • compare_ │ │
│ │ • suggest_ │ performance│ │
│ │ improvements│ │ │
└─────────────┴─────────────┴─────────────┴─────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ MCP Resources & Prompts │
├─
Truncated for display — read the full file on GitHub.
Related Skills
Agent-Reach
85.0kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
headroom
73.6kCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
ruflo
73.1k🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
CowAgent
47.1kOpen-source super AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-agent, multi-model, multi-channel. Lightweight, extensible, one-line install.
Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
