Mcp Jenkins Intelligence
About
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.
Explore
- 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
- Anomaly Detection: AI-powered detection of unusual pipeline behavior patterns
- Comprehensive Insights: AI-generated insights and recommendations
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Mcp Jenkins IntelligenceCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Prefer a ready-to-use binary? Download the latest release and start using MCP Jenkins Intelligence in seconds!
curl -fsSL https://raw.githubusercontent.com/heniv96/mcp-jenkins-intelligence/main/install.sh | bash
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! 🎉
---
- 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
#
For detailed installation and setup instructions, see the Quick Start Guide.
TL;DR:
git clone https://github.com/heniv96/mcp-jenkins-intelligence.git
cd mcp-jenkins-intelligence
pip install -r requirements.txt
pip install -e .
``
For detailed configuration options, see the Configuration Guide.
Quick Reference:
- Authentication: Standard Jenkins or Azure AD
- Environment Variables: JENKINS_URL, JENKINS_USERNAME, JENKINS_TOKEN
- Port Configuration: Customizable MCP server port
- Command Line Options: , --port, --verbose`
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request
pip install -e ".[dev]"
- Getting Started: See installation section above for detailed setup
- Configuration: Authentication options and environment variables
- Troubleshooting: Common issues and solutions
- API Reference: Complete tool documentation
Category
Tools
get_pipeline_metrics
Get detailed metrics (success rate, duration, frequency)
get_pipeline_dependencies
Get upstream/downstream pipeline dependencies
monitor_pipeline_queue
Monitor Jenkins build queue and pending builds
analyze_build_trends
Analyze trends across multiple pipelines
predict_pipeline_failure
AI prediction of failure risk based on patterns
suggest_pipeline_optimization
AI suggestions for performance optimization
detect_pipeline_anomalies
Detect unusual patterns and anomalies in pipeline behavior using ML
intelligent_retry_logic
Analyze failure patterns and suggest intelligent retry logic using ML
generate_ai_insights
Generate comprehensive AI-powered insights about the pipeline using real data analysis
scan_pipeline_security
Security vulnerability scan and best practices check
audit_access_controls
Audit pipeline access controls and permissions using Jenkins security API
get_jenkinsfile
Get Jenkinsfile (automatically reconstructs if stored in Git)
reconstruct_jenkinsfile
Reconstruct Jenkinsfile content from pipeline execution data
suggest_pipeline_improvements
Get improvement suggestions based on reconstructed Jenkinsfile
generate_pipeline_report
Generate comprehensive pipeline report based on Jenkins build history
compare_pipeline_performance
Compare performance across multiple pipelines based on Jenkins build history
analyze_build_time_optimization
Analyze build time optimization opportunities using Jenkins data
| Category | Tools | Description |
|----------|-------|-------------|
| Core Pipeline Operations (5) | list_pipelines, get_pipeline_details, get_pipeline_builds, analyze_pipeline_health, analyze_pipeline_failure | Direct Jenkins API operations, monitoring, and analysis |
| AI-Powered Analysis (1) | ask_pipeline_question | Natural language queries and intelligent insights |
| Configuration Management (3) | configure_jenkins, test_connection, get_server_info | Jenkins connection setup, validation, and server information |
✅ Safety Note: All tools are read-only or analysis-only operations that cannot modify Jenkins state. This ensures safe operation without risk of accidentally triggering builds or changing pipeline configurations.
| Tool | Description |
|------|-------------|
| get_pipeline_metrics | Get detailed metrics (success rate, duration, frequency) |
| get_pipeline_dependencies | Get upstream/downstream pipeline dependencies |
| monitor_pipeline_queue | Monitor Jenkins build queue and pending builds |
| analyze_build_trends | Analyze trends across multiple pipelines |
| Tool | Description |
|------|-------------|
| predict_pipeline_failure | AI prediction of failure risk based on patterns |
| suggest_pipeline_optimization | AI suggestions for performance optimization |
| detect_pipeline_anomalies | Detect unusual patterns and anomalies in pipeline behavior using ML |
| intelligent_retry_logic | Analyze failure patterns and suggest intelligent retry logic using ML |
| generate_ai_insights | Generate comprehensive AI-powered insights about the pipeline using real data analysis |
| Tool | Description |
|------|-------------|
| scan_pipeline_security | Security vulnerability scan and best practices check |
| audit_access_controls | Audit pipeline access controls and permissions using Jenkins security API |
| Tool | Description |
|------|-------------|
| get_jenkinsfile | Get Jenkinsfile (automatically reconstructs if stored in Git) |
| reconstruct_jenkinsfile | Reconstruct Jenkinsfile content from pipeline execution data |
| suggest_pipeline_improvements | Get improvement suggestions based on reconstructed Jenkinsfile |
| Tool | Description |
|------|-------------|
| generate_pipeline_report | Generate comprehensive pipeline report based on Jenkins build history |
| compare_pipeline_performance | Compare performance across multiple pipelines based on Jenkins build history |
| Tool | Description |
|------|-------------|
| analyze_build_time_optimization | Analyze build time optimization opportunities using Jenkins data |
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp jenkins intelligence": {
"mcp-jenkins-intelligence": {
"command": "python",
"args": [
"-m",
"pytest"
]
}
}
}
}McpServers
{
"mcp-jenkins-intelligence": {
"command": "python",
"args": [
"-m",
"pytest"
]
}
}
> 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
```bash
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