AI INTEGRATION FOR JENKINS
Jenkins MCP Server
Enable AI agents to interact with Jenkins through Model Context Protocol for automated DevOps workflows
Project Overview
The Jenkins MCP Server is a groundbreaking integration that enables Jenkins to serve context and operations through the Model Context Protocol (MCP). This project bridges the gap between AI agents and Jenkins, allowing for unprecedented levels of automation and AI-assisted DevOps workflows.
By implementing the MCP protocol, Jenkins becomes accessible to LLM-based tools and AI assistants, enabling natural language interactions with your CI/CD infrastructure.
- Query job configurations
- Trigger builds programmatically
- Access build logs and artifacts
- AI-assisted troubleshooting
Quick Start
# Clone the repository
git clone https://github.com/avisangle/jenkins-mcp-server.git
cd jenkins-mcp-server
# Install dependencies
pip install -r requirements.txt
# Configure Jenkins connection
export JENKINS_URL="http://your-jenkins-server:8080"
export JENKINS_USER="your_username"
export JENKINS_TOKEN="your_api_token"
# Run the MCP server
python -m jenkins_mcp_serverFor Claude Desktop integration and advanced configuration, see the README on GitHub.
What It Does
Query job configurations, retrieve status information, and manage Jenkins jobs through AI-powered natural language commands.
Trigger builds, monitor execution status, access build logs, and retrieve artifacts programmatically through the MCP interface.
Provide AI agents with comprehensive Jenkins environment information for intelligent decision-making and automation.
Why It Matters
Transform Jenkins operations with AI assistance. Use natural language to interact with your CI/CD infrastructure, making complex operations accessible to everyone.
Reduce time spent on repetitive Jenkins tasks. AI agents can handle routine operations, troubleshooting, and monitoring automatically.
MCP provides a standardized protocol for AI-Jenkins interaction, enabling integration with any MCP-compatible LLM application or AI assistant.
Respects Jenkins' authentication and authorization models, ensuring secure AI interactions that maintain your existing security policies.
Implementation Highlights
MCP Server Architecture
Python-based MCP server with Node.js wrapper for seamless integration with Jenkins. Optimized for low-latency responses to support real-time AI conversations.
Security First
Respects Jenkins' authentication and authorization models, ensuring secure AI interactions that maintain existing security policies.
High Performance
Optimized for low-latency responses with efficient caching and connection pooling for real-time AI conversations.
Extensible Design
Modular tool system allows custom extensions for specific use cases. Add new capabilities without modifying core functionality.
- Job configuration queries and status retrieval
- Programmatic build triggering with parameters
- Build log access and real-time monitoring
- Artifact retrieval and management
- Jenkins environment context provisioning
- Custom tool development framework
- Secure authentication integration
- RESTful API compatibility
Related Projects
Explore the Code
Check out the GitHub repository for implementation details and documentation