Skip to content

Releases

v1.0.1 - Initial Public Release

v1.0.1 - Initial Public Release Pre-release
Pre-release

Choose a tag to compare

View all tags
@karadhub karadhub released this 02 Nov 19:07
v1.0.1
ea0f02f

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog,
and this project adheres to Semantic Versioning.

1.0.1 - 2025-11-02

🎉 Initial Release

This is the first public release of Jenkins AI Optimizer - an enterprise-grade Model Context Protocol (MCP) server for Jenkins CI/CD integration.

✨ Features

Core Jenkins Operations

  • Multi-Instance Management: Connect and manage multiple Jenkins servers from a single configuration
  • Build Information Retrieval: Get comprehensive build metadata including status, duration, and artifacts
  • Job Parameter Discovery: List and understand configurable parameters for any Jenkins job
  • Build Triggering: Start new builds with optional parameters, both synchronously and asynchronously
  • Async Build Monitoring: Trigger builds and monitor completion status automatically

Intelligent Diagnostics

  • AI-Powered Failure Analysis: Automatic categorization of build failures using configurable regex patterns
  • Error Pattern Recognition: Pre-configured patterns for compilation, timeout, permission, and dependency errors
  • Contextual Recommendations: Actionable suggestions based on failure type
  • Data Extraction: Automatically extracts error codes, version numbers, file paths, and timestamps
  • Custom Pattern Support: Extensible diagnostic configuration for organization-specific error patterns

Advanced Log Analysis

  • Contextual Log Fetching: Retrieve specific log sections with line number ranges
  • Error Filtering: Extract error lines using pattern matching
  • Ripgrep Integration: Fast regex search across logs with context lines
  • Interactive Navigation: Navigate logs by sections with offset support
  • Streaming Processing: Handle multi-gigabyte logs without memory issues

Vector-Powered Semantic Search

  • Natural Language Queries: Search logs using natural language instead of exact keywords
  • Qdrant Integration: High-performance vector database for embedding storage
  • Chunk-Based Indexing: Efficient processing and searching of logs of any size
  • Configurable Models: Uses all-MiniLM-L6-v2 by default with customization options

Pipeline Analysis

  • Sub-Build Tree Traversal: Automatically discovers and maps complex pipeline hierarchies
  • Recursive Discovery: Finds all nested builds in multi-stage pipelines
  • Status Mapping: Highlights failure points in pipeline trees
  • Build Duration Tracking: Records execution time at each pipeline level

Performance & Reliability

  • Smart Caching: Compresses and stores frequently accessed logs locally with LRU eviction
  • Automatic Cleanup: Scheduled maintenance of cache and vector store
  • Connection Pooling: Efficient HTTP session management with automatic retry logic
  • Health Monitoring: Built-in health endpoints for Docker deployments
  • Configurable Timeouts: Per-instance timeout settings for different Jenkins servers

Developer Experience

  • FastMCP Framework: Built on modern async Python with FastAPI
  • Multiple Transport Modes: Supports both stdio and HTTP/SSE transports
  • Dependency Injection: Clean architecture with testable components
  • Comprehensive Logging: Configurable logging levels with structured output
  • Docker Support: Production-ready Docker Compose deployment
  • Configuration Validation: Automatic validation of YAML configuration files

🏗️ Architecture

  • MCP Server: FastMCP-based server with HTTP/SSE and stdio transport
  • Tool Factory: Dynamic tool instantiation with dependency injection
  • Multi-Jenkins Manager: Intelligent routing to appropriate Jenkins instances
  • Cache Manager: Local filesystem cache with compression and LRU eviction
  • Vector Manager: Qdrant client for semantic search operations
  • Cleanup Scheduler: APScheduler-based maintenance tasks

📦 Available Tools

  1. diagnose_build_failure - AI-powered build failure analysis
  2. get_build_info - Comprehensive build metadata retrieval
  3. get_job_parameters - List configurable job parameters
  4. trigger_build - Start builds with parameters
  5. trigger_build_async - Async build triggering with monitoring
  6. get_log_context - Fetch specific log sections
  7. filter_errors - Extract error lines from logs
  8. ripgrep_search - Fast regex search across logs
  9. navigate_log - Interactive log navigation
  10. search_logs_semantic - Vector-based semantic search
  11. get_sub_build_tree - Pipeline hierarchy traversal

🐳 Deployment Options

  • Docker Compose: Production-ready deployment with Qdrant
  • Local Development: Virtual environment with pip installation
  • Claude Desktop Integration: Direct MCP connection via SSE

📚 Documentation

  • Comprehensive README with architecture diagrams
  • Detailed configuration guides
  • Docker deployment instructions
  • Diagnostic pattern documentation
  • API usage examples
  • Troubleshooting guides

🔧 Configuration

  • YAML-based configuration for Jenkins instances
  • Per-instance settings (timeouts, SSL verification, log size limits)
  • Diagnostic pattern customization
  • Vector search configuration
  • Cache management settings
  • Cleanup scheduling options

🧪 Testing

  • Integration tests with real Jenkins instances
  • MCP protocol validation tests
  • Performance benchmarks
  • Tool scenario testing
  • Error handling validation

📝 Requirements

  • Python 3.10 or higher
  • Docker and Docker Compose (for production deployment)
  • Jenkins instance(s) with REST API access
  • Jenkins API token(s)
  • Qdrant (included in Docker Compose, optional for local dev)

🔐 Security

  • Token-based authentication for Jenkins
  • Per-instance SSL verification settings
  • No credentials stored in code
  • Configuration file-based secret management
  • GPL v3.0 license

🙏 Acknowledgments

  • FastMCP for modern MCP server framework
  • Qdrant for high-performance vector database
  • Anthropic for Model Context Protocol specification
  • Jenkins Community for robust CI/CD platform

[Unreleased]

Planned Features

  • GitHub Actions integration
  • GitLab CI/CD support
  • Enhanced AI diagnostics with LLM integration
  • Build artifact analysis
  • Performance metrics dashboard
  • Webhook support for real-time updates
Loading