It feels like everyone’s talking about MCP (Model Context Protocol) these days when it comes to Large Language Models (LLMs), but hardly anyone is actually defining it. Let’s go deeper!

The context problem for LLMs

LLMs struggle when they are asked for information outside of their training data. They’ll almost always either hallucinate and say something incorrect, or simply say, “I don’t know.” Giving them the right amount of context they need when you prompt them (whether it’s your codebase, your repository data, your documentation, etc.) is necessary for AI agents built on top of them to be useful.

Usually, to give LLMs that context, you have to really refine your prompting, or use some sort of external tool. For example, something like GitHub Copilot has tools like @workspace to give relevant information from your codebase to your prompts. This type of “extra tooling” is cool, but can get fairly complex fairly quickly.

The solution: MCP

In November, Anthropic open-sourced the Model Context Protocol as a standard for connecting LLMs and AI assistants to data and tools!

MCP grew the way you sleep… slowly and then all at once. As tools and organizations have adopted the MCP standard, it’s only become more and more valuable. And because MCP is model-agnostic, anyone can use and create MCP integrations. As with all open standards, a rising tide lifts all boats: the more people use it, the better it becomes.

I think that MCP has “won” the hearts of so many AI developers and tools because of this openness, and also because it’s a very “AI-first” version of existing ideas.

Back in 2016, Microsoft released the Language Server Protocol, which provided standards for code editors to support programming languages. Fast forward to today, because of LSP, programming language support across editors is better than ever, to the point where it’s not even a consideration for developers to have to think about anymore!

MCP takes a lot of its inspiration from LSP, and could be absolutely transformative for AI tooling. It will allow for everyone, from the largest tech giants to the smallest indie developer shops, to enable robust AI solutions in any AI client with minimal setup.

That’s why this is a huge deal! An open standard that is backed more and more by the tech community will mean better tools, better developer experiences, and better user experiences for everyone.

I’m sold! How do I contribute and learn more?

Hoorah, I thought you’d never ask! Here’s some resources to get you on your way: