Codifying Context: Experiments in the MCP Protocol

One thing I’ve learned building AI assistants: they have the memory of a goldfish. They’ll forget important details as soon as the conversation moves on. I got tired of my AI asking me for the same info over and over, so I started developing something I grandly call the Model Context Protocol (MCP). In simple terms, MCP is an attempt to give my AI a working long-term memory—a way to remember context and facts across interactions.

The idea is to codify context into a structured format that any part of my system can understand. Think of it like a shared language for all the components of my AI. Instead of the AI only knowing what’s in the last message, it refers to an ongoing “context file” of key facts and history. I’ve been experimenting with how to build and update this file on the fly. For example, if I tell Coach (my AI assistant) today that my dog’s name is Luna, MCP should ensure that tomorrow Coach still knows who Luna is without asking again. It’s like an internal wiki that the AI consults as needed.

Building MCP hasn’t been straightforward. I’ve tried a few approaches: plain text logs, summarizing past chats into notes, even a vector database to embed and retrieve relevant pieces of conversation. Each has its pros and cons. The challenge is striking a balance between too much information (which can confuse the AI) and too little (which defeats the purpose). Early tests are promising, though. With a solid context protocol, my AI’s responses stay more consistent and “aware” of past details. It feels more like an attentive friend and less like a stranger with each new session.

There’s still a lot to refine. How should the AI decide what to save to its memory file and what to omit? What’s the best way to quickly fetch the right memory when needed? I’m treating this as an ongoing experiment. But one thing’s clear: giving an AI a better memory makes it feel way more intelligent. And if I can crack the code on context, it might just reshape how these agents handle knowledge and continuity in the long run.