AI is like working with an inept wizard. (Yes, I have a lot of metaphors for this.) When you ask the wizard a question, he responds with the intellect and rapidity of someone who has access to the knowledge of the cosmos. He’s read everything, but he’s a bit dotty. He’s lived his entire life in his lair, consuming his tomes. Despite his vast knowledge, he has no idea what happened in the world yesterday. He doesn’t know what’s in your inbox. Moreover, he knows nothing about your contact list, your company’s proprietary data, or the fact that your cousin’s birthday party got bumped to next Friday. The wizard is a genius. He’s also an idiot savant.
Therein lies the paradox. We have designed amazing tools, but they require a lot of handholding. Context has to be spoon-fed. You can paste an entire mountain of reference documents and a virtual novel of a prompt. That amount of work can often eliminate any benefit you get from using an LLM at all. When it does work, it’s a victory but it feels like you’ve wrestled the LLM into submission instead of working with it.
Users have been cobbling together ad hoc solutions for this problem. Plug-ins. Vector databases. Retrieval systems. These Band-Aids are clever, but fragile. They don’t cooperate with each other. They break when you switch providers. It’s less “responsible plumbing” and more “duct tape and prayer.”
This is where Model Context Protocol (MCP) comes in. It establishes a foundational infrastructure rather than creating one more marketplace for custom connectors. MCP sets up standardized rails for integrating context. This shared framework enables models to request context, retrieve it from authorized sources, and securely use it. It replaces the current kluge of vendor-specific solutions with a unified protocol designed to connect AI to real world systems and data.
As AI transitions from an experimental novelty to practical infrastructure, this utility becomes crucial. For the wizard to be effective, he needs to be able to do more than solve one-off code hiccups or create content for your blog. For true usefulness at scale in a professional environment you need a standardized way to integrate context. That context has to respect permissions, meet security standards, and be up to date.
The Problem of Context in AI
Models tend to make things up and they do it with confidence. Sometimes they cite fictional academic papers. Sometimes they invent dates, statistics, or even people. These hallucinations are a huge problem, of course, but they’re a symptom of a much larger issue: a lack of context.
The Context Window Problem
Developers have been trying to develop workarounds by providing relevant data as needed. Pasting in documents, providing chunks of a database, and formulating absurdly robust prompts. These fixes are great, but every LLM has what we call a context window. The window determines how many tokens a model can remember at any given time. Some of the bigger LLMs have windows that can accommodate hundreds of thousands of tokens, but users still quickly find ways to hit that wall.
Bigger context windows should be the answer, right? But there’s our Catch 22: The more data you provide within that window, the more fragile the entire set up becomes. If there’s not enough context, the model may very well just make stuff up. If you provide too much, the model bogs down or becomes too pricey to run.
The Patchwork Fixes
The AI community wasn’t content to wait for one of the big players to provide a solution. Everyone rushed to be first-to-market with an assortment of potential fixes.
Custom plug-ins let the models access external tools and databases, extending their abilities beyond the frozen training data. You can see the issue here. Plug-ins designed for one platform won’t work with another. Your workspace becomes siloed and fragmented, forcing you to rework your integrations if you try to switch AI providers.
Retrieval Augmented Generation (RAG) converts documents to embed them into a vector database so that you can pull only the most relevant chunks during a query. This method is pretty effective but requires significant technical skills and ongoing fine-tuning based on your organization’s specific requirements.
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