patchy631/ai-engineering-hub
AI Engineering Hub offers in-depth tutorials and practical resources on Large Language Models, Retrieval-Augmented Generation, and real-world AI agent applications for all skill levels.
Awesome MCP › MCP Resources & Educational Materials
decodingai-magazine/enterprise-mcp-course
The project "Designing Enterprise MCP Systems" is an open-source initiative aimed at helping developers build modular, production-grade AI automation systems using the Model Context Protocol (MCP). The focus of this series is on creating a Pull Request Reviewer Assistant for enterprise projects that integrates with tools like GitHub, Slack, and Asana. This assistant analyzes GitHub pull requests, extracts context from GitHub and Asana, and delivers actionable insights to teams via Slack, showcasing a real-world enterprise use case of MCP. The project teaches how to automate AI workflows within internal systems and build scalable infrastructure that supports multiple automation pipelines, making it suitable for organizational growth. It also provides guidance on evaluating MCP for enterprise migration, helping teams decide if transitioning to an MCP-based architecture is beneficial. Key learning outcomes include building custom MCP servers for Slack and Asana, connecting to external MCP servers such as GitHub Remote MCP, centralizing tools and prompts into a global MCP Tool Registry, and creating custom MCP Hosts to orchestrate workflows without relying on third-party desktop clients. The course emphasizes designing and scaling company-wide automation workflows, starting with the PR Reviewer use case. The target audience includes ML/AI engineers, software engineers, and DevOps/MLOps engineers who want to orchestrate AI tools, build scalable and secure automation workflows, and apply best practices in software engineering and prompt engineering to production AI systems. The prerequisites are intermediate Python skills, beginner knowledge of REST APIs and web development, and a basic understanding of AI/LLM concepts. The repository is structured with separate directories for MCP hosts and clients, modular MCP servers, and static resources like architecture diagrams. The project is designed to be accessible, with no GPU requirements and the ability to complete the course at zero cost using the Gemini free tier. Overall, this project provides a comprehensive, practical guide to building enterprise-ready AI automation systems using the Model Context Protocol, focusing on scalability, integration, and real-world application.
https://github.com/decodingai-magazine/enterprise-mcp-course
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