Open-source project by Decoding ML
The Enterprise MCP Series is an open-source initiative designed to help you build modular, production-grade AI automation systems using the Model Context Protocol (MCP).
In this series, we focus on building a Pull Request Reviewer for an enterprise project, showcasing how to integrate multiple tools and orchestrate them into a scalable, production-ready MCP architecture.
Unlike simple tutorials, this series tackles real-world, enterprise use cases—like a Pull Request Reviewer Assistant that analyzes GitHub PRs, pulls context from Github and Asana, and posts actionable insights to your team on Slack.
It shows how to:
- Automate AI workflows within your internal systems
- Build scalable infrastructure – design workflows that support multiple automation pipelines, ready to grow with your organization
- Evaluate MCP for enterprise migration – understand if migrating your codebase to an MCP-based architecture is worth it
You will learn how to:
- Build custom MCP Servers for Slack and Asana to expose enterprise tools and resources
- Connect to external MCP servers (e.g., GitHub Remote MCP) and integrate them seamlessly
- Centralize tools and prompts into an internal Tool Registry (a global MCP server)
- Create a custom MCP Host to orchestrate workflows (no reliance on Claude Desktop)
- Design and scale company-wide automation workflows, starting with the PR Reviewer use case
With these skills, you'll become a pro 🥷 at building enterprise-ready AI automation systems using MCP, designing your own scalable hosts, integrating internal tools, and orchestrating intelligent workflows across your company.
| Target Audience | Why Join? |
|---|---|
| ML/AI Engineers | Learn to orchestrate multiple AI tools, agents, and resources across your organization |
| Software Engineers | Build scalable, maintainable, and secure automation workflows |
| DevOps & MLOps Engineers | Apply best practices in software engineering, MLOps, and prompt engineering to production AI systems |
| Category | Requirements |
|---|---|
| Skills | - Python (Intermediate) - REST APIs & Web development (Beginner) - Basic understanding of AI/LLM concepts (Beginner) |
| Hardware | Modern laptop/PC (no GPU required – all servers run locally or in lightweight containers) |
| Level | Begginer/Intermediate (anyone willing to learn can follow along) |
By using the Gemini free tier, this course can be completed at zero cost.
| Lesson | Title | Focus |
|---|---|---|
| 1 | Why MCP Breaks Old Enterprise AI Architectures | Architecting the solution and understanding the MCP mindset. |
| 2 | Build with MCP Like a Real Engineer | Implementing the full PR Reviewer Assistant workflow end-to-end. |
| 3 | Getting Agent Architecture Right | Exploring other agent patterns and workflow architectures for scalable PR review automation. |
enterprise-mcp-series/
├── apps/
│ ├── pr-reviewer-mcp-host/ # MCP custom host & client
│ └── pr-reviewer-mcp-servers/ # Modular MCP servers (GitHub, Slack, Asana, etc.) & Tool Registry
├── static/ # Architecture diagrams, images
├── LICENSE
└── README.md Find detailed setup instructions in each app's documentation:
| Application | Documentation |
|---|---|
| MCP Host & Client Connection Manager |
apps/pr-reviewer-mcp-host |
| Modular MCP Servers (GitHub, Slack, Asana, Prompts) & Tool Registry |
apps/pr-reviewer-mcp-servers |
Have questions or running into issues? We're here to help!
Open a GitHub issue for:
- Technical questions
- Troubleshooting
- Suggestions or contributions
As an open-source course, we may not be able to fix all the bugs that arise.
If you find any bugs and know how to fix them, support future readers by contributing to this course with your bug fix.
You can always contribute by:
- Forking the repository
- Fixing the bug
- Creating a pull request
We will deeply appreciate your support for the AI community and future readers 🤗
 Anca Ioana Muscalagiu SWE/ML Engineer |
 Paul Iusztin AI/ML Engineer |
This project is licensed under the MIT License - see the LICENSE file for details.
