How to Use the GitHub MCP Server: A Complete Guide

TDLR Article Summary

GitHub’s MCP server implements Anthropic’s MCP protocol for connecting platforms to AI. Using it, you can query your code repos, create branches and PRs, and manage issues. While you can install the server locally and manage it yourself, many enterprises benefit from hosted offerings. Workato’s hosted offering doesn’t just manage the server for you; it also makes it easy to connect it to your other enterprise offerings.

Imagine an AI assistant that not only talks about or generates code but also navigates your repositories, creates and updates issues, and even manages pull requests. 

That’s the capability that the Model Context Protocol (MCP)—an open standard that bridges the gap between large language models (LLMs) and developer tools—unlocks.

In this post, we’ll describe the GitHub MCP Server. It’s GitHub’s implementation of this standard and acts as a universal adapter that securely exposes GitHub’s API primitives to an AI. 

We’ll also show how to take advantage of Workato’s hosted MCP server offering to deploy a GitHub MCP server in minutes.

What Is the GitHub MCP Server?

In November 2024, Anthropic published the Model Context Protocol, an open standard for integrating LLMs with external tools, development environments, and data sources. 

A large number of vendors, including OpenAI, Google, Microsoft, and GitHub, adopted this open standard.

GitHub’s MCP Server exposes its extensive API to LLM clients, including Claude Desktop, Cursor, Antigravity, and VS Code. This makes it possible for those AI applications to navigate repos, look at files, track issues, and manage code context.

How Does the GitHub MCP Server Work?

You can run your own instance of GitHub’s MCP Server, connect to GitHub’s cloud service, or use a hosted offering like Workato’s. In all cases, the server communicates with clients via a JSON-RPC 2.0-based protocol. 

Your AI client provides reasoning and intent, while the MCP server acts as a control plane for executing API requests against GitHub.

What Are the GitHub MCP Server Capabilities?

When you connect your AI tools to GitHub’s MCP Server, you gain these abilities.

1. Repository Management & Code Exploration

Your LLM can retrieve file contents, view directories, search code, and push files to your repositories.

2. Issues Management

Your AI tools can automate the triage, creation, and maintenance of bug reports and feature tracking.

3. Pull Requests & Code Review

You can use your LLM to manage your branch/review/merge lifecycle via the GitHub MCP Server.

4. GitHub Actions & CI/CD Intelligence

You can also connect your AI to automated delivery pipelines to aid in automated builds and debugging.

5. Advanced & Miscellaneous Utilities

The MCP server provides access to creating and retrieving GitHub Gists. It also acts as an interface to Dependabot alerts, code scans, and scanning alerts.

Run GitHub MCP Server Locally or in the Cloud?

Before you install and connect to a GitHub MCP Server, you need to decide where it’s going to live. Should you run it locally or in the cloud? 

The cloud is the easiest option for getting up and running quickly, while a local copy gives you more control over where your data resides.

Workato offers fully hosted, production-grade MCP infrastructure that simplifies integrating GitHub with a variety of applications.

Why Use Workato’s Managed GitHub MCP Server?

Cloud Infrastructure

When you use Workato’s MCP infrastructure, they host the server instances directly in their cloud. 

So, whether you’re a developer or an IT administrator, you don’t need to install Docker infrastructure or worry about the complex configuration a local instance requires. Workato handles the hosting, scaling, and maintenance for you.

Zero-Key Management & Unified Authentication

GitHub’s local MCP server requires personal access tokens (PATs), which require periodic rotation and represent a significant security risk if compromised. 

Workato centralizes authentication with secure OAuth connections to GitHub. It wraps all your API requests to GitHub with verified user access (VUA)—meaning any action an AI agent takes on GitHub inherits the explicit corporate identity and permissions of the user prompting it.

Turn Recipes into Hosted MCP Tools

Instead of just exposing raw GitHub API endpoints, Workato allows you to turn any existing Workato Recipe Function or API Management (APIM) Collection into a remote, hosted MCP server with a single click.

Getting Started with Workato’s MCP Server

Let’s go over how easy it is to get a GitHub MCP Server running with Workato.

Prerequisites

First, you’ll need an account with Workato and, of course, an account and credentials for GitHub. Workato supports three authentication schemes with GitHub:

• OAuth authentication. This is the easiest method, since you only need to log in to GitHub via Workato, allowing it to act on your behalf.
• A GitHub Apps connection. This allows Workato recipes to act as a GitHub application with your credentials. You can read more about this authentication scheme here.
• Personal access tokens. While using PATs via Workato is more secure than storing them in local files, they’re still more difficult to manage than OAuth.

For this tutorial, we’ll use OAuth since it’s the most straightforward.

1. Sign in to your Workato account and navigate to the project you’ll be using for your MCP Server.
2. Next, click Create>Connection and select GitHub
3. Give the connection a descriptive name.
4. Now, use the Location menu to assign the connection to a project.
5. Click on the Authentication type dropdown and choose OAuth app.
6. Finally, click connect. Workato will route you to GitHub. You’ll authenticate with them and provide authorization to act on your behalf.

Creating an MCP Server

Now, exit the connections screen and head to Workato’s AI Hub. From there, select MCP Servers, click the plus (+) and select Create MCP Server.

From there, navigate to the pre-built MCP servers and select the one that suits your needs. Give it a name, and select a project in the Location field.

Now, go to the connections section and connect your app account using the name you provided for the connection above. Your GitHub MCP Server is complete.

What Can You Do With Your GitHub MCP Server?

Now that you’ve deployed your GitHub MCP server with Workato, you can use recipes to connect it to a variety of different enterprise applications. 

Let’s look at a concrete example of what you can do when you connect an LLM to an MCP server, a ticketing platform such as Jira, and a messaging system like Teams.

The Problem: Engineering Escalation and Support Triage

Say an enterprise client discovers a critical product issue and contacts customer support. Support verifies the problem and enters a ticket into Jira.

Now, a developer has to read the ticket, go to GitHub and create a development issue, create a branch, and update their group via Teams. All these steps take time and leave room for error. Let’s see how we can streamline this process.

The Solution: LLM Automation via Workato

Let’s consider the same situation, but with a recipe that connects an LLM, such as Claude Desktop or Cursor, to all three applications.

In this scenario, the developer sees the ticket and enters this prompt:

“Review Jira ticket #XX, look up the corresponding code failure in XXX repo, and initialize an escalation.”

This initiates the following:

1. The LLM fetches the ticket from Jira. It contains a stack trace and an urgency level.
2. Next, the AI uses the GitHub MCP Server to search for the code that generated the stack trace. Then it creates an issue, highlighting the potential problem area.
3. Finally, it generates a Teams message alerting the development team of the new issue.

So, what would have taken a developer at least a quarter of an hour, but often much longer, is reduced to a simple interaction with an LLM.

But if you’re not comfortable with automations or AI, you might be looking for the guardrails in this process. What’s keeping the system from creating spurious issues that waste developer time?

You can add those protections in the form of approval steps. For example, Workato can send an interactive prompt to an engineering manager requesting approval before creating the new GitHub issue.

Get Started with GitHub MCP Server

GitHub’s implementation of the MCP protocol provides your AI tools with direct access to your code repositories and development issues. 

Workato adds even more value with a hosted implementation that you can spin up in minutes and easily connect to your development tools and other enterprise applications. Sign up for a trial today and connect your code repos to the enterprise.

FAQS

What Is MCP in GitHub?

The Model Context Protocol (MCP) is an open standard for connecting large language models (LLMs) to external tools and platforms. 

In the GitHub ecosystem, MCP makes it possible for AI agents to interact with GitHub repositories. So, instead of developing custom tools, you can use a GitHub MCP Server to query your code.

Is There an MCP Server for GitHub?

Yes, GitHub has an MCP Server. You can download it for free and run it locally or take advantage of hosted offerings from providers like GitHub and Workato. 

The local version will run inside a Docker container or via a local build and NodeJS. Enterprise platforms manage the hosting and updates for you.

What Is the Difference Between GitHub MCP and GitHub CLI?

The GitHub CLI is designed for interactive use by an engineer, while the MCP Server is for LLMs to connect via an API connection.

It’s possible for an LLM to use the CLI, and some users prefer that since they can more easily see what the LLM is doing. But many consider MCP a better option since it is expressly intended for AI connections.

This post was written by Eric Goebelbecker. Eric has worked in the financial markets in New York City for 25 years, developing infrastructure for market data and financial information exchange (FIX) protocol networks. He loves to talk about what makes teams effective (or not so effective!).