Modern AI agents are getting smarter — but they’re still terrible collaborators. Each model operates in its own bubble and have:

• No memory of past steps
• No awareness of other agents
• No shared understanding of the user’s goal

This leads to what we call context fragmentation — a major bottleneck in building multi-agent AI systems, assistants, or tools that actually work together.

That’s where MCP (Model Context Protocol) comes in. MCP is a lightweight standard that lets AI agents share a common context, synchronize goals, and reason together. This article explores what MCP is, how it fits into the AI landscape, and how you can set up a simple MCP server to power context-aware AI agents like ChatGPT.

• MCP: The Tech behind Context-Aware AI
• MCP Clients vs Servers
• When Should You Use MCP?
• How to Set Up an MCP Server (Conceptually)
  • 1. Choose Your Context Storage
  • 2. Expose the required API
  • 3. Connect Your Agents (Like ChatGPT)
• Example workflow setup
• Next Steps

MCP: The Tech behind Context-Aware AI

MCP stands for Model Context Protocol — a communication standard that allows AI agents, tools, or models to share and synchronize contextual information in real time.

At its core, MCP is not about "controlling" agents — it's about helping them coordinate.

You can think of it like a shared bulletin board or workspace:

• Agents can write things they’ve learned (“user wants a summary of today’s news”)
• Other agents can read and respond accordingly (“I'll fetch articles from news sources”)
• Context evolves over time, but is centrally visible to all participants

This allows multiple agents — or even different components of a single system — to remain aligned and avoid redundant work or misinterpretations.

MCP Clients vs Servers

MCP arctitectures follow a client-server protocol with clear distinction of roles:

MCP Server:

• Stores key-value pairs representing session memory, tasks, agent states, etc.
• Handles incoming context updates and serves current context on request.
• Typically lightweight and stateless — think of it like a Redis or in-memory document store.

MCP Clients:

• These are your AI agents, tools, or wrappers (like ChatGPT).
• Clients send context updates (e.g., “current task = booking a flight”) and subscribe to changes.

This architecture is intentionally simple and general-purpose — allowing you to plug in agents running different models, on different stacks, written in different languages.

When Should You Use MCP?

You don’t need MCP for a simple chatbot that answers one-off questions. The role of MCP only becomes prominent if you're building multi-step agents, autonomous systems, or anything involving tool use.

Use MCP if you need:

• A way for agents to share user goals, task history, or intermediate state
• Real-time collaboration between multiple AI models (e.g., one for retrieval, one for summarization)
• A shared “working memory” across a session, device, or organization
• Better observability into what agents are doing and why

Example Scenarios:

• A ChatGPT agent that delegates parts of a task to specialized tools (calendar lookup, email summarization)
• An AI-powered assistant with memory that persists across sessions
• Agents that coordinate tasks in a workflow engine (e.g., LangGraph or Autogen)

How to Set Up an MCP Server (Conceptually)

Let’s walk through what it means to set up an MCP server — without diving into code.

1. Choose Your Context Storage

Your MCP server needs a place to store context. This could be:

• An in-memory store (e.g., Python dictionary if prototyping)
• A Redis or key-value database (for reliability and scale)
• A document store like MongoDB (if you want structured memory per session)

Example structure for key-value pairs (most common):

{
  "user_intent": "book a flight",
  "last_tool_used": "calendar-checker",
  "session_notes": "user prefers morning flights"
}

2. Expose the required API

Your MCP server acts like a context registry. Agents interact with it via simple HTTP endpoints, such as:

• POST /context — to set or update context values
• GET /context/{agent_key} — to retrieve a specific context item

For example, an agent might send:

POST /context
Content-Type: application/json

{
  "key": "user_goal",
  "value": "summarize latest news articles"
}

3. Connect Your Agents (Like ChatGPT)

Once your server is live, you configure your agents (like a ChatGPT plugin or wrapper) to:

• Push updates when their internal state changes
• Read context before taking any action

Example workflow setup

Once your MCP server is running and storing shared context, multiple AI agents can collaborate smoothly by reading from and writing to this shared memory. Imagine a travel-planning scenario with three agents:

User says: "Help me book a business trip to San Francisco."

1. ChatGPT Assistant writes to MCP:

{
  "user_intent": "plan business trip to San Francisco",
  "current_task": "search flights"
}

2. Flight Search Bot reads user_intent and current_task from MCP, performs flight queries, then writes:

{
  "flight_options": ["Flight A", "Flight B", "Flight C"],
  "current_task": "select flight"
}

3. ChatGPT Assistant reads updated flight_options from MCP, presents them to the user, and waits for selection.

4. Once the user selects a flight, ChatGPT Assistant updates MCP:

{
  "selected_flight": "Flight B",
  "current_task": "book calendar"
}

This cycle continues, with all agents sharing and synchronizing context via the MCP server — enabling a smooth, multi-step, multi-agent workflow without direct coupling.

Next Steps

If you're excited by the idea of multi-agent systems and smarter AI workflows:

• Check out frameworks like LangGraph or Autogen
• Try building your own MCP-compatible tool
• Read up on OpenAI's ChatGPT tools API

And if you're building your own agent infrastructure, adding an MCP server might be the best way to give your agents shared context — and shared purpose.

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