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.
TL;DR: It’s an open standard for connecting LLMs to data and tools.
Let’s dive in deeper!
The context problem for LLMs
LLMs often struggle when they are asked for information outside of their training data. They’ll sometimes either hallucinate and say something incorrect, or simply say, “I don’t know.”
Giving them the right amount of context when you prompt them (whether it’s your codebase, your repository data, your documentation, etc.) is necessary for AI agents built on top of LLMs to be useful.
Usually, you have to really refine your prompting to give LLMs that context, or use some sort of external tool. For example, 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 as you implement things across different APIs and services.
A solution: Model Context Protocol, or 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 has 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 that 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.
This isn’t the first time we’ve seen a protocol like this become a standard, either. Back in 2016, Microsoft released the Language Server Protocol (LSP), 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 developers don’t even need to think about it anymore!
MCP takes a lot of its inspiration from LSP, and could be absolutely transformative for AI tooling. It allows 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 means better tools, better developer experiences, and better user experiences for everyone.
GitHub and MCP
We’re not just talking about MCP: we’re contributing, too!
We’re SO excited to have recently released our new open source, official, local GitHub MCP Server! It provides seamless integration with GitHub APIs, allowing for advanced automation and integration capabilities for developers to build with!
You can chat more with us about it in the GitHub Community or you can check out the official announcement.
How do I contribute and learn more?
Hoorah, I thought you’d never ask! Here’s some resources to get you on your way:
- MCP documentation
- Repository of reference implementations for MCP
- MCP specification for protocol requirements
- More documentation on LSP
Also, if you don’t mind the shameless plug, you can also use it with agent mode now. Go forth and code!
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