Wei-Meng Lee

If you've spent any time around large language models (LLMs), you've probably heard the term “fine-tuning” used to mean about six different things. Some people use it to describe writing a really good prompt. Others use it to describe attaching a small classifier on top of a frozen model. Others mean updating every single weight in a 7-billion-parameter model.

Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models communicate with external tools and data sources. If you are new to MCP, CODE Magazine has already published a previous article MCP: Building the Bridge Between AI and the Real World that covers the fundamentals in detail. In this article, I will take a more hands-on approach, focusing on practical code samples and walking you through how to write your own MCP server from scratch.

Large language models (LLMs) are no longer limited to cloud environments. With tools like Ollama, developers can run powerful language models directly on their own machines, gaining full control over data, costs, and performance. Cloud-based services from providers such as OpenAI and others have made LLMs widely accessible, but they require sending prompts and data to external servers, which can raise concerns around privacy, compliance, and data ownership.