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MCP: A Language for Smarter AI Agents

By Joe Seifi 0 comments • about 1 month ago

Modern coding agents still struggle with one key problem: they don’t understand your world. They might be trained on vast amounts of data and seem smart in general, but when it comes to the specifics of your project—your codebase, tools, workflows—they often fall short. They forget what they just saw, they lose context between sessions, and they can’t keep up with how your environment evolves.

This isn’t because the models are bad. It’s because of how they’re used. Most large language models (LLMs) today are stateless and remote. They process each task in isolation, without memory of your setup unless you repeatedly tell them everything all over again. Tools like retrieval-augmented generation (RAG) help by stuffing relevant files into the prompt, but this is a short-term fix—not a long-term foundation.

A better pattern is starting to emerge: a dual-model architecture. One small model runs locally—fast, persistent, tuned to your environment. One larger model runs in the cloud—powerful, general, trained on the world. The local model understands the details. The remote model handles the reasoning. Together, they can do more than either can alone.

But to make this work, the models need to speak a common language.


What Is MCP?

MCP, or Model Context Protocol, is that common language. It’s not a model, and it’s not a training method. It’s a protocol: a structured format for how AI agents, tools, and memory systems exchange information.

Think of MCP as the API contract between AI systems. It defines:

  • Roles for each agent (e.g., planner, executor, retriever)
  • Structured context like goals, state, memory, and available tools
  • Composable interfaces that allow one agent’s output to become another’s input

It’s simple at its core: a way to organize what agents know, what they’re doing, and how they collaborate.


The HTTP Analogy

If you’ve built for the web, you know what HTTP did. It gave browsers and servers a shared way to communicate. Before HTTP, everything was custom. After HTTP, you could build websites, APIs, and distributed apps.

MCP is aiming to do the same for AI agents.

Web (HTTP)AI Agents (MCP)
Browser sends a requestLocal agent sends a task or goal
Server returns HTML/JSONRemote model returns a plan or result
HTTP defines endpoints + verbsMCP defines roles, memory, and tools
Middleware adds cookies, authMCP adds task history, goal chaining

Just as HTTP turned the internet into an interoperable ecosystem, MCP can turn individual models into a collaborative system.


Why It Matters

AI tooling today is like the early internet—messy, handcrafted, and not built for scale. Prompts are fragile. Memory is an afterthought. Every tool reinvents context handling from scratch.

MCP introduces a shared format that:

  • Makes agents composable, like services
  • Makes context reusable, like shared state
  • Makes memory persistent, like a backend

Most importantly, MCP makes it possible for local and remote agents to work together without losing track of what they're doing.


A Simple Yet Powerful Architecture

Here’s how this can look in practice:

🔹 Local Model (Edge Agent)

  • Runs on your device (Phi-3, Mistral, TinyLlama)
  • Knows your filesystem, dev tools, local context
  • Fast, persistent, and always nearby

🔸 Remote Model (Cloud Agent)

  • Runs on powerful hardware (GPT-4, Claude 3, Gemini)
  • Handles complex reasoning, synthesis, planning
  • Optimized for general knowledge and large context

🔁 The Glue: MCP

  • Connects both agents with a shared task interface
  • Encodes what they’re doing, what they know, and what tools they have
  • Supports layered memory, chaining, and tool orchestration

If this sounds like the architecture of a progressive web app, that’s intentional: responsive on the edge, intelligent in the cloud, unified by protocol.


Who’s Building Toward This?

Many teams are working on parts of this stack:

  • Cody, Continue.dev, Cursor: local dev agents with project awareness
  • LangChain, AutoCodeRAG, Dust: orchestration layers with tool integration
  • OpenDevin, Devika, Cognition: autonomous AI developers with memory

But the full MCP-based system—local + remote + structured protocol—is still in progress. The pieces are here, but the architecture hasn’t gone mainstream yet.


What Do We Call It?

This class of systems goes by many names:

  • Dual-Layer Dev Agent
  • Edge-Orchestrated LLM
  • Progressive Agent Stack
  • Local-Augmented AI
  • Or simply: MCP Agents

Regardless of the name, the concept is the same: a smarter, more modular way for models to work together—and remember what they’re doing.


Final Thought: From Models to Systems

The future of AI won’t be just about bigger models. It will be about better systems. Models that don’t just generate, but collaborate. That don’t just respond, but remember. That don’t just work alone, but work together.

We’re moving from a world of individual prompts to a world of orchestrated cognition. From raw language prediction to distributed intelligence.

And just like HTTP made the web possible, MCP might be the thing that makes intelligent systems actually feel intelligent.

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