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.

What It Does

This MCP server implementation for Jenkins enables external clients—particularly LLM-based tools and AI agents—to interact programmatically with Jenkins jobs, builds, and operational context. The integration provides:

  • Built-in Tools: Includes essential tools such as whoAmI, job status queries, and build log retrieval
  • AI Agent Integration: Allows AI assistants to understand Jenkins state and execute operations through natural language
  • Context Sharing: Exposes Jenkins context to external systems through standardized MCP interfaces
  • Extensible Architecture: Supports custom tool development for specific CI/CD workflows

Why It Matters

Traditional CI/CD interactions require manual intervention or complex scripting. The Jenkins MCP Server transforms this paradigm by:

  • Enabling conversational interactions with Jenkins through AI agents
  • Reducing the learning curve for complex Jenkins operations
  • Accelerating troubleshooting through AI-powered log analysis
  • Creating opportunities for intelligent automation beyond traditional scripting
  • Establishing a foundation for next-generation DevOps tooling

Implementation Highlights

Technical Architecture

The project is built with Python and Node.js, providing a robust MCP server implementation that integrates seamlessly with Jenkins. Key architectural decisions include:

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MCP Server Architecture

Python-based MCP server with Node.js wrapper for seamless integration with Jenkins

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Security First

Respects Jenkins' authentication and authorization models, ensuring secure AI interactions

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High Performance

Optimized for low-latency responses to support real-time AI conversations

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Extensible Design

Modular tool system allows custom extensions for specific use cases

Core Capabilities

  • Job Management: Query job configurations, trigger builds, and monitor execution status
  • Build Operations: Access build logs, retrieve artifacts, and analyze build results
  • Context Awareness: Provide AI agents with comprehensive Jenkins environment information
  • Tool Extensibility: Support for custom tool development to extend functionality

Demo & Usage

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Demo video and screenshots coming soon

This section will showcase the Jenkins MCP Server in action, including:

  • AI agent querying Jenkins job status
  • Triggering builds through natural language commands
  • Analyzing build logs with AI assistance
  • Integrating with popular LLM tools

Technology Stack

Backend: Python 3.12+
Runtime: Node.js 14.0+
Protocol: Model Context Protocol (MCP)
Libraries: FastAPI, Pydantic, Requests, Cachetools

Explore the Code

The Jenkins MCP Server is open source and available on GitHub. Check out the repository to see the implementation details, contribute to the project, or use it in your own Jenkins setup.

View Repository on GitHub →