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Learn how maintainers are using the GitHub MCP Server and what they are building in this episode of the GitHub Podcast.
Imagine you’re coding in VS Code with Copilot agent mode. You ask it: “What’s the status of PR #72?” But instead of fetching the details from GitHub, the model hallucinates an answer based on outdated context or guessed semantics. It sounds convincing, but it’s just wrong. Models, after all, are only as good as the context given to them. If large language models (LLMs) don’t have the ability to connect to other apps and tools, they’re not as useful as they can be.
This is the core problem that Model Context Protocol (MCP) was built to solve.
MCP is an open protocol that standardizes how LLM apps connect to and work with your external tools and data sources. It is similar to the Language Server Protocol (LSP) in that both follow client-server architecture, they simplify interaction between systems, and they provide standardized communication patterns. In essence, MCP is the LSP of LLMs.
And now, GitHub has open-sourced its own MCP server. It acts as a source-of-truth interface between GitHub and any LLM, reducing hallucinations and unlocking new automation workflows.
We cover this (and more!) in our latest episode of the GitHub Podcast! Listen below 👇
We put together a guide on everything you need to know (but were too embarrassed to ask) about MCP.
MCP is based on a client-server architecture where an MCP host — an AI app like Copilot Chat — maintains a dedicated 1:1 connection with MCP servers. Some key concepts to understand:
The GitHub MCP Server connects AI tools directly to GitHub’s platform. Instead of performing REST or GraphQL API calls, you point your MCP-compatible client or agent to the server, and request exactly what you need. For example, you could ask it to:
The magic is that you can now use natural language to make requests that are automagically converted into structured, semantically meaningful API calls. You’re no longer creating custom API endpoints or parsing markdown descriptions. Ask for what you need in natural language to fetch real-time data from GitHub.
And because the server speaks MCP, it can work with any compatible host. Copilot Workspace, VS Code plugins, LLM-based products, custom chat UIs, and homegrown agents can all request context or trigger actions using the same standardized interface.
The architecture is conceptually simple, but powerful:
When a user asks a question, the host translates the question into a semantic request, the client packages it as an MCP request, and the server fetches the real data from GitHub and returns it as structured JSON.
This creates a clean separation between the language model, the UX, and the data or tools it can access. Each layer is modular, testable, and swappable.
The best part: You can start using GitHub’s MCP server today! Here’s what you need:
To install the GitHub MCP Server in VS Code follow these steps:
You’re now ready to use the GitHub MCP server in VS Code!
Watch this video where I installed the GitHub MCP Server and automagically created five issues with natural language!
Early adopters have used MCP servers to create useful tools.
By providing real, structured context to AI models, MCP servers enable tools that are both smarter and safer.
Read our practical guide on how to use the MCP server for a detailed walkthrough. Want to dive deeper? See how to build a secure and scalable remote MCP server in this guide.
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