MCP: Building the Bridge Between AI and the Real World

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.” … ### 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. … Each of these approaches are useful, but they all suffer from the same weakness as any proprietary solution. The developers have to reinvent the wheel each time. Without universal integration standards, these solutions are unstable and non-transferable. AI systems need a standardized approach for context access and authentication. … ## Security, Privacy, and Governance Is MCP the holy grail of making functional AI agents—without using a bottle of Elmer's glue and some yarn to bundle the integrations together? If your internal security alarms are ringing, that just means you're thinking responsibly. Every time I read about one of these new AI applications, I cringe at the security implications. A workflow that makes it easier to move context across systems also expands your exposure surface. Such a system has to prioritize security, privacy, and governance. … ### The Governance Layer Although security and privacy are vital, use of MCP brings up some complicated questions regarding governance. We're still in the Wild West phase of AI and as it continues to evolve, we'll remain there. It can be a crap shoot determining which servers can be trusted. How can an organization of any size know where to set boundaries? How do we determine what the model is allowed to access? … - **Context Poisoning:** If a malicious actor can compromise an MCP server, they can manipulate the data flowing to the model, corrupting it. Transparency can provide visibility to the data, but it's unable to filter out tainted information. - **Overreach:** It's tempting for an organization to default to maximum connectivity. Maybe it opts togive the AI assistant far more access than is truly needed. That plants the seeds for an inevitable breakdown in governance. - **Surveillance Misuse:** The protocol has no inherent bias, but the use of it will define the outcomes. There's always a chance of abuse. In such a scenario, a malicious user could weaponize MCP to aggregate and surveil sensitive user information. - **Ecosystem Fragmentation:** There's always the possibility that MCP won't be fully adopted but cloned. MCP-like variations could fragment the landscape and cut compliance corners. Interoperability breaks down and erodes security assurances. ### Juggling Openness and Safety Therein lies the friction: The openness and flexibility of MCP leads to a more powerful ecosystem. But with that openness comes increased risk. How are servers vetted? Soon, we'll see them popping up all over the place. Some of them will be compromised. It's just the law of numbers. How can users ensure that these upstart servers won't leak, corrupt, or abuse data?