Taming the Beast: How to Stop GitHub Copilot From Ingesting Your Entire Hard Drive
Z
Zack Saadioui
8/11/2025
Taming the Beast: How to Stop GitHub Copilot From Ingesting Your Entire Hard Drive
Look, I love GitHub Copilot. You love GitHub Copilot. It's like having a junior developer on tap 24/7 who never complains about your messy code, never needs a coffee break, & is surprisingly good at writing boilerplate. But let's be honest, sometimes it feels less like a helpful "copilot" & more like a back-seat driver with a bad case of ADHD.
We've all been there. You run a massive test suite, your terminal explodes with thousands of lines of output, & suddenly Copilot's suggestions become... weird. It starts suggesting random snippets from the test logs, gets obsessed with a particular error message from 20 minutes ago, or worse, just completely loses the plot. The same thing happens with large files. You open a giant JSON fixture or a generated CSS file, & Copilot's context window gets so stuffed with irrelevant junk that it can't see the actual code you're trying to write.
It's a classic case of "garbage in, garbage out." The AI is only as good as the context it's given, & when we feed it a firehose of terminal logs, dependency folders, & massive data files, its performance nosedives. It's frustrating, it's a waste of time, & it makes you want to just turn the whole thing off.
But what if you could put your Copilot on a diet? What if you could tell it, "Hey, see this terminal output? Just give me the important bits. And this 10MB log file? Yeah, just ignore that completely."
Turns out, you can. It's a bit of an "insider" trick, but it's possible to take back control & make Copilot work for you, not against you. We're going to dive deep into how to do this, starting with the ultimate power move: running your own custom MCP server.
The Root of the Problem: Copilot's Cluttered Context Window
Before we get into the "how," let's talk about the "why." The heart of this issue is something called the "context window." In the world of Large Language Models (LLMs) like the one powering Copilot, the context window is like its short-term memory. It's the collection of text—your open files, your cursor position, your recent code—that the model looks at to figure out what you're trying to do & generate a relevant suggestion.
The bigger & more relevant the context, the better the suggestions. But here's the catch: the context window has a finite size. It can only hold so much information at once. When you flood it with irrelevant data, you're pushing out the good stuff.
Think of it like trying to have a conversation with someone in a room with a dozen TVs blaring at full volume. It's hard for them to focus on what you're saying when they're being bombarded with noise. That's what we're doing to Copilot when we let it ingest things like:
Verbose Terminal Output: Build scripts, test runners, & package managers can generate thousands of lines of logs. Most of it is noise. The only thing that usually matters is the final error message or the summary at the end.
Large Data Files: JSON, CSV, or XML files used for testing or fixtures can be huge. Copilot doesn't need to know the entire structure of a 50,000-line JSON file to help you write a function that fetches data from it.
Dependency Folders: Your
1
node_modules
or
1
vendor
directory is a black hole of context. Copilot should be looking at your code, not the minified source of a library you're using.
Build Artifacts: Minified JavaScript, compiled CSS, & other generated files are not useful for context & can seriously confuse the AI.
When the context window is full of this junk, Copilot's suggestions get worse, it uses more processing power, & it can even get stuck in loops, trying to "fix" a problem that's actually just a noisy log message.
The Ultimate Fix: Running a Custom MCP Server
Alright, so how do we fix this? The most powerful, albeit most technical, solution is to create your own Model Context Protocol (MCP) server.
That sounds intimidating, I know. But stick with me. An MCP server is essentially a middleman that sits between your IDE (like VS Code) & GitHub Copilot. It's a way to hook into the conversation Copilot is having & give it new tools & capabilities. Instead of Copilot just blindly reading your files & terminal, it can ask your MCP server for help.
Recently, a developer on Reddit, frustrated with this exact problem, built an open-source MCP server specifically designed to filter out the noise. It's a brilliant example of what's possible. Their server provides a set of "tools" that Copilot can use to be more precise. Here are a few examples of the kinds of tools you could build:
1
runAndExtract
: This is the magic wand for terminal spam. Instead of just running a command & dumping the entire output into the context, this tool runs the command, then uses another LLM to analyze the output & pull out only the important information, like the final error message or a summary of the results. The raw log is never sent to Copilot.
1
askAboutFile
: Instead of opening a massive file & feeding the whole thing to Copilot, this tool lets Copilot ask a specific question about the file's contents. For example, "What are the keys in this JSON object?" The tool then reads the file, finds the answer, & sends back just that small, relevant snippet.
1
askFollowUp
: This lets you have a conversation about the last terminal output without having to re-run the command every time, saving you time & keeping your context clean.
Pretty cool, right? By building a server with these kinds of tools, you're essentially giving Copilot a set of smarter senses. It can now "hear" the important parts of the terminal output & "see" the relevant lines in a large file without getting distracted by the noise.
Now, building your own MCP server from scratch does require some development know-how. You'd typically write it in something like TypeScript or C#, & you'd need to understand the Model Context Protocol specification. GitHub has some documentation to get you started, & there are community projects you can look at for inspiration.
But the core idea is revolutionary. It's about personalizing your AI assistant to fit your specific workflow. It's about giving it the tools it needs to be a truly effective partner.
This is actually pretty similar to what we're seeing in the business world. Companies are realizing that generic, one-size-fits-all AI isn't enough. They need AI that understands their specific products, services, & customers. That's where platforms like Arsturn come in. Arsturn helps businesses build no-code AI chatbots trained on their own data. Instead of a generic bot that gives generic answers, a business can create a custom AI that can provide instant, personalized support, answer detailed questions about their products, & engage with website visitors 24/7. It's the same principle: making AI smarter by giving it better, more relevant context.
Practical, No-Code Ways to Tame Copilot
Okay, so maybe you're not ready to spin up your own MCP server just yet. I get it. The good news is, there are still a TON of things you can do to improve Copilot's performance without writing a single line of code.
Here are the most effective strategies, starting with the simplest.
1. Master Your
1
.gitignore
This is the lowest-hanging fruit. GitHub Copilot is designed to respect your project's
1
.gitignore
file. This means that any file or folder you list in
1
.gitignore
will be ignored by Copilot for context. This is your first line of defense against context pollution.
Your
1
.gitignore
should already include things like
1
node_modules
, but you can & should add more. Here's a good starting point for a typical project: