🤖 Model Context Protocol (MCP) - Technology Not to be Underestimated

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The Model Context Protocol (MCP) is a standard for enabling communication between different systems — AI models, intelligent agents, software tools, databases, and applications. Its purpose is to organize and structure context provided to an AI model (like an LLM), so it can better understand the situation and respond more accurately and usefully.

If you want to learn more about AI, I recommend this article "Essential concepts for understanding AI".

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🤔 Why does MCP exist?

When you use an LLM, you give it a prompt like:

"Recommend a hotel in Paris for a couple, under €200 per night."

But what does the model know about you? Nothing. It doesn't know if you've traveled before, if you prefer spa hotels, or what you searched for previously. If you want it to know, you need to manually cram all this info into your prompt. This is fragile, inefficient, and not scalable.

🔧 MCP fixes this

With MCP, you can feed the model all this data in a structured way, like filling out a JSON form. The LLM receives not just the question, but an organized context, with typed, structured information bound to a specific scope.

But there's more: MCP is also a protocol for system interconnection. It allows AI models, software tools, applications, and data sources to talk to each other using a common standard. This means an AI agent can:

  • receive data from an ERP system

  • query a semantic knowledge base

  • access external tools (e.g., calculators, APIs)

  • and generate intelligent outputs

All of this without having to reinvent how context is passed or interpreted every time.


🧪 A practical example

Without MCP

You send the model this prompt:

I'm a 30-year-old living in New York. 
I searched for hotels in Paris yesterday but found nothing interesting.
Recommend something new under €200 per night.

With MCP

You send this JSON:

{
  "scope": "travel_recommendation",
  "context": [
    { "type": "user_profile", "data": { "age": 30, "location": "Milan" } },
    { "type": "search_history", "data": ["paris 4-star hotel", "paris spa"] },
    { "type": "budget_preference", "data": "max €200/night" }
  ],
  "input": "Recommend something new"
}

The model already knows everything — and can use the context intelligently, without you having to re-explain.


🧭 MCP in the modern AI landscape

Concept How It Relates to MCP
LLM MCP provides consistent, modular context to improve responses
RAG MCP structures retrieved content (e.g., from a vector DB) in a clear, standardized way
Vector Database Retrieved documents are encapsulated as search_result objects in the MCP context
Agentic AI Each agent can operate within a scope, retain memory, state, and access tools via MCP
Tool use / plugins MCP enables interoperable descriptions of available tools

🏗️ Visual Architecture Diagram

MCP acts as the common language that orchestrates all these components.

source https://modelcontextprotocol.io/introduction


🚀 Why MCP changes the game

AI is evolving from a phase where “models respond to requests” to a phase where models collaborate with complex systems, take on roles, make decisions, use tools, and adapt over time.

To make this work, a protocol is needed. MCP is that protocol.

Thanks to MCP:

  • AI models can interact with other software systems like microservices

  • It's possible to build ecosystems of AI agents working together

  • We unlock large-scale integration between AI and enterprise, industrial, or personal applications

  • Design becomes more modular, reusable, and interoperable

In short: MCP is the lingua franca of future AI systems.


⚠️ MCP and the risks of hyperconnected AI: governance is essential

While MCP enables a promising future of integration and automation, it also introduces complex and potentially risky scenarios. When multiple intelligent systems start collaborating autonomously, who controls the information flow? Who ensures an agent doesn't act on outdated, manipulated, or inappropriate context?

Real risks include:

  • Misuse of context: a system might access sensitive data or use it outside its intended scope

  • Excessive autonomy: agents talking to each other unsupervised might behave unpredictably

  • Decision opacity: when context is built from distributed sources, tracing why a model responded in a certain way becomes hard

  • Reliance on poorly implemented standards: inconsistent MCP adoption can lead to fragmentation, incompatibility, and vulnerabilities

Why governance matters

Governance isn't about slowing innovation, but about making it sustainable and safe. It must ensure that:

  • every agent has a defined scope and controlled access

  • context is validated, logged, and traceable

  • tools exist for auditing inter-system interactions

  • there's transparency around who built and modified the context

How to implement it

  • Access policy and scope management for agents

  • Formal standardization of MCP, with versioning and validators

  • Context logging as a semantic trace (e.g., who provided which piece of information, when and why)

  • Sandboxes to test multi-agent behavior before deployment

 

Ultimately, MCP holds massive potential — but only if paired with ethical, design-aware, and regulatory foresight. Without it, we risk building a web of autonomous intelligences with no compass or brakes.


✅ Summary

  • MCP is a standard for describing and sharing context between systems

  • It allows AI models, tools, data, and agents to communicate in a structured way

  • It enhances LLMs, RAG systems, agentic AI, and complex integrations

  • It enables a future of connected, autonomous, and scalable AI

  • But it also requires strong governance to ensure safety, transparency, and control

  • Just as JSON enables APIs to exchange data in a predictable format, MCP enables AI systems and agents to exchange contextual information in a consistent and interoperable way.

    MCP is to AI systems what JSON is to APIs: a structured, standardized way to exchange meaningful data.


If you're starting to explore AI, understanding MCP puts you a step ahead in building intelligent, integrated — and responsible — systems.


References



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