8/12/2025

How to Handle Large Codebases with MCP Without Crashing Your System

Alright, let's talk about something that gives even seasoned developers a headache: massive codebases. You know the ones. The monoliths that have been around for years, with layers upon layers of code, where a single

git pull

feels like it might just bring your machine to its knees. Trying to get an AI coding assistant to do anything useful in a repo like that? Forget about it. The AI gets lost, runs out of context, & you end up spending more time explaining the code than it would have taken to write it yourself.

But here's the thing. There's a new piece of tech on the block that's quietly changing the game, & it's called the Model Context Protocol, or MCP. If you're tired of your system chugging every time you try to do some serious work on a large project, you're going to want to stick around. We're going to dive deep into what MCP is, how it works, & most importantly, how to use it to tame those beastly codebases without melting your CPU.

So, What Exactly is This MCP Thing?

MCP, or Model Context Protocol, is an open standard that was open-sourced by the folks at Anthropic. In the simplest terms, it's a universal translator between AI models & external data sources. Think of it like a USB-C port for AI. Before, if you wanted to connect an AI to a new tool or database, you had to build a custom, one-off integration. It was a messy, time-consuming process. MCP creates a standardized way for these connections to happen, making it MUCH easier to give an AI access to the information it needs to be genuinely helpful.

The architecture is pretty straightforward. It's a client-server model.

The MCP Host: This is the AI application you're using, like an AI-powered IDE or a chatbot.
The MCP Client: This lives inside the host & is responsible for connecting to the servers.
The MCP Server: This is where the magic happens. It's a lightweight program that exposes a specific tool or data source to the AI. You could have an MCP server for your codebase, another for your database, & another for your company's internal documentation.

This modular approach is what makes MCP so powerful. You can chain together different servers to give your AI a comprehensive understanding of your entire project, not just the single file you have open.

Why MCP is a Game-Changer for Huge Repos

The biggest problem with using AI on large codebases is the "context window." AI models have a limited amount of information they can "see" at any given time. When your codebase is millions of lines long, there's no way to cram all of that into a single prompt. This is why AI assistants often give generic or just plain wrong answers when working on complex projects. They're missing the bigger picture.

MCP solves this by allowing the AI to query for the specific information it needs, right when it needs it. Instead of trying to stuff the entire codebase into the context window, the AI can use an MCP server to:

Perform a semantic search: Find relevant code snippets based on meaning, not just keywords.
Analyze the file structure: Understand how the project is organized.
Look up dependencies: See how different parts of the code are connected.
Read documentation: Get context on how a specific function is supposed to be used.

This is the difference between giving an AI a single page of a book & giving it access to the entire library. The quality & relevance of its output go through the roof.

Getting Your Hands Dirty: A Look at an MCP Server

Okay, theory is great, but let's get practical. How do you actually set up an MCP server? The good news is, you don't have to be a wizard. There are already a bunch of pre-built servers & tools that you can use, & the community is building more every day.

For example, there are MCP servers that can connect to your local filesystem, your Git repository, or even your Figma designs. Tools like "Claude Context" use a vector database to make your entire codebase searchable, which is a HUGE performance win. Instead of loading massive files into memory, it only pulls the most relevant chunks of code based on what you're asking.

Here's a super-simplified look at what a bare-bones MCP server that reads a file might look like in Python. This is just to give you an idea of the moving parts; you'd want to use one of the available SDKs to build a real one.