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Posted on Jun 23 • Originally published at codanyks.hashnode.dev

Stop Duct-Taping Context Into Prompts: Meet the MCP Server

#ai #mcp #programming #node

Learn how the Model Context Protocol (MCP) can power smarter, scalable, and stateless AI agent systems.

If you’ve ever built anything slightly more complex than a simple chat interface with an LLM, you’ve probably run into the same wall most of us hit:

Agents forget things.
Context doesn’t persist.
Coordination feels clunky.
Everything breaks when scale enters.

Welcome to the context crisis.

The era of building LLM-powered tools and agents is here — but our infrastructure isn’t ready. Most devs are duct-taping memory, state, and agent logic into isolated silos, and calling it a day. But you know that won’t scale.

What if you could separate the logic of what a system should do from what it knows and how it remembers?

That’s where the Model Context Protocol (MCP) comes in — and no, this isn’t just a fancy wrapper. It’s an architectural pattern that might just save your stack.

Let’s go deep.

What Is MCP, Really?

At its core, an MCP server is a centralized system responsible for managing and serving structured context to agents, tools, and orchestrators in a multi-agent or AI-enhanced application.

Think of it as the memory-and-goalkeeper for your AI systems.

MCP doesn't run inference. It doesn’t respond like ChatGPT. Instead, it responds like a protocol-bound librarian:

“You are the planner. You’ve been working on Task #42. Here’s your role, memory, and available tools. Good luck.”

It’s like a router for meaning — sitting in the middle, handing out purpose, memory, identity, and task state.

Why Context Needs Its Own Server

Let’s walk through a few realities of agent-based development:

Agents need to know who they are, what their role is, and what’s been done so far.
You might be dealing with tool-using agents, each with different objectives.
Memory isn't just logs; it’s a structured and evolving state.
You want clean interfaces, separation of concerns, and scalable logic.

But here's what usually happens:

const agentPrompt = `
  You are AgentPlanner. Your task is to break down user goals into steps.
  Prior goal: ${goal}
  Past memory: ${memory}
`

You start cramming context into prompts. You start caching state into local files. You start coupling memory logic to inference code. Suddenly, everything is fragile.

MCP breaks that pattern.

It says: Let the agents focus on thinking. Let the MCP server handle knowing.

What Does an MCP Server Actually Do?

Let’s break it down.

✅ Identity Resolution

Every agent has an identity: a name, a role, maybe a persona or tone. The MCP server tells agents who they are.

✅ Memory Abstraction

MCP offers structured memory: past inputs, decisions, events, user feedback. It can be task-scoped or global.

✅ Goal Distribution

Agents may be working toward shared or solo goals. MCP tracks and distributes these dynamically.

✅ Contextual Routing

Need tools? Previous state? Team member roles? MCP routes the right payloads to the right agents.

Anatomy of an MCP Request

Here’s a sample POST request from an agent to the MCP server:

POST /context

{
  "agent_id": "planner-001",
  "task_id": "goal-execution-42",
  "request_type": "context_bundle"
}

And here’s what the MCP might return:

{
  "persona": "PlannerGPT",
  "system_prompt": "You are an expert task planner...",
  "memory": {
    "past_steps": [...],
    "user_feedback": "Focus on frontend components."
  },
  "tools": ["search", "task-scheduler"],
  "next_steps": ["Draft UI plan", "Assign tasks to DesignerGPT"]
}

This is not inference — this is intelligent scaffolding. It allows agents to stay stateless and sharp.

Real-World Analogies

Let’s say you’re building an indie RPG with multiple characters, quests, and evolving storylines.

In game dev, you wouldn’t make every NPC hardcode the player’s current state, completed quests, or world state, right?

You’d centralize that. You’d have a state manager.

That’s what MCP is — but for agents.

It's the "quest manager" of your AI story. Agents query it to get the world state, their role, their memory.

Now take that metaphor and apply it to an LLM-based assistant that uses Codex, a browser plugin, and a memory engine.

Boom. You need an MCP server.

Architectural Pattern

+----------------+      +--------------+      +------------------+
|  Frontend App  | ---> |  MCP Server  | ---> |  Agent Inference |
+----------------+      +--------------+      +------------------+
                             |
                             v
                       [Memory Store]
                             |
                             v
                      [Goal Repository]

The MCP server sits in between, handling structured API requests and serving agents clean, focused context.

It connects memory, goals, identity, and available tools to the active session.

In later articles, this pattern expands to support agent-initiated context fetching, agent-to-agent coordination, and shared blackboard communication models — all mediated through the MCP.

Without MCP: What Goes Wrong

Agents lack history or carry too much of it.
You can’t easily rotate or upgrade memory providers.
You’re locked into brittle prompt-chaining.
Debugging context bugs becomes a nightmare.
You get burned out managing the glue instead of building the product.

Why Now?

The agent ecosystem is exploding. Frameworks like AutoGen, CrewAI, LangGraph, and open-agent stacks are maturing.

But they all suffer from the same issue: context is poorly managed.

As AI architectures evolve, context routing is poised to become a new standard — and MCP servers offer a practical way to start building around it.

What You’ll Learn in This Series

This is just Day 1.

Here’s what’s coming next:

Day 2: “Build Your Own MCP Server (In TypeScript)”

Create a functioning MCP Server
Define context schemas
Build routes for /context and error handling
Modularize memory and goal repositories

Day 3: “Let Agents Pull Their Own Context”

Build agent-side context fetchers
Add autonomy to inference layers
Eliminate reliance on UI requests

Day 4: “Enable Agent-to-Agent Communication”

Agents that write and read context from each other
Shared blackboard model
Delegation and feedback loops

Day 5: “The Wrap-Up: Tools, Patterns, Libraries”

Design tradeoffs
Tooling suggestions
Future directions for multi-agent systems

Build for Scale, Even When Small

Even if you're just experimenting, building with protocol-thinking can save you from rewrites down the line.

MCP doesn’t just improve developer experience — it opens the door for truly modular, scalable, agent-based applications.

Stop hardcoding memory into your agents.

Start thinking in systems.

Build weird, but build right.

Stay tuned for Day 2.

And remember → LLMs are the actors. MCP is the director.🎬

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