🔆 MCP: Powering the Next Wave of AI Agents

🗞️ Issue 71 // ⏱️ Read Time: 7 min

Hello 👋

If you've used tools like ChatGPT, you've interacted with a Large Language Model (LLM). These powerful AI models are a core technology behind many of the Generative AI (GenAI) systems you see today, great at processing and generating human-like text based on the massive data they were trained on.

However, even these seemingly intelligent LLMs have limitations. Without direct access to external tools and a standardized way to use them, they can't inherently perform actions in the real world or access information beyond their training set. This is where MCP comes in.

The Model Context Protocol (MCP) is an open standard developed by Anthropic that acts as a "universal connector" for AI applications. MCP provides a standardized way for LLMs and AI agents to interact with external tools, data sources, and systems, whether it’s a weather API, a CRM, or a custom analytics engine. This effectively strapped rockets onto LLMs in terms of intelligence and capability 🚀

How MCP Works: Speaking AI’s new language

Think of an LLM before MCP like a brilliant speaker who only knows one language. They can communicate perfectly with others who speak the same language, but they're limited when trying to interact with people who speak different languages (like me, trying to order a pain au chocolat in France).

MCP is like giving that AI a universal language translator system. This system has several parts working together:

• MCP Hosts (The "User Interface" for the AI): These are programs or AI tools (like Claude Desktop or IDEs) where the AI is operating. They know when the AI needs to interact with the outside world and how to initiate a request using the standardized MCP language.

• MCP Clients (The "Translator's Assistant"): These are components within the system that maintain the connection to the actual translators. When a Host has an MCP request, the Client sends it along.

• MCP Servers (The "Translators" for Specific Topics): These are lightweight programs that each specialize in translating the standardized MCP language for a specific set of external resources. Think of them as translators who are experts in financial data, or experts in calculator operations, or experts in accessing local files.

• Local Data Sources & Remote Services (The Information the AI Wants to Talk To): These are the actual sources of information or tools the AI needs to interact with. This could be data on your computer (Local Data Sources) or services on the internet via APIs (Remote Services). The MCP Server (the translator) knows how to communicate with these using their specific "languages."

This is the core of MCP: the standardized MCP language used by the AI Host, managed by Clients, translated by Servers, to interact with Local Data Sources and Remote Services.

Just like a traveler might use different language translators for different countries or topics, the AI can use the MCP Host/Client system to connect to multiple MCP Servers, each providing translation for different types of information or tools.

How MCP Changed the Game

Anthropic released the Model Context Protocol in November of 2024, and it’s been getting hyped ever since. Before MCP, it was difficult to build AI agents that could perform complex tasks involving external systems. Developers had to create a custom setup for the AI to work with each tool or data source. This was like needing to carve a brand-new, custom key for every single door in your house.

This led to several pain points:

• Limited Scalability: Adding new capabilities was time-consuming due to custom integrations.

• Brittleness: Changes in external systems could easily break the AI's connection.

• Lack of Generalization: AI agents were often limited to the specific tasks and systems they were built for.

MCP fundamentally changes this by providing a common, standardized way for AI to interact with the outside world. This "universal key" approach makes AI agents much more flexible and capable. By easily connecting to external tools and data sources, MCP allows AI to:

• Access Fresh, Real-Time Information: Go beyond static training data to get the latest updates.

• Perform Actions in the Real World: Use tools like calculators, search engines, or business software.

• Improve Accuracy and Reduce Errors: Ground responses in verifiable external data.

• Handle Complex Tasks: Pull in necessary context from multiple sources as needed.

MCP is accelerating the development of more powerful, reliable, and versatile AI agents that can dynamically learn and act in the real world, transforming their potential impact across various applications.

MCP in Action: Rapid Adoption & Growing Ecosystem

The impact of MCP is already being felt across the industry. Following its release, the protocol saw rapid developer uptake, with hundreds of MCP servers implemented quickly, enabling faster and more reliable AI application development.

Leading AI providers were quick to embrace the standard. By early 2025, major industry support was evident, with adoption from key players including OpenAI, Google DeepMind, and Microsoft. 

This rapid adoption has fueled a significant ecosystem expansion. A growing number of development tools, platforms, and companies across various sectors are implementing MCP, demonstrating its growing role as a crucial piece of the AI infrastructure.

A Real-World Business Example

Imagine a customer service AI agent. Before MCP, this agent might only be able to pull information from a limited internal knowledge base.

With MCP, this agent could:

1. Receive a customer query about an order.

2. Use MCP to connect to the company's order database and retrieve the customer's order status (accessing data).

3. If the order is delayed, use MCP to interact with the shipping carrier's system to get real-time tracking information (using an external tool via a service).

4. If the customer wants to change the shipping address, use MCP to connect to the customer relationship management (CRM) system and update the address (performing an action via a service).

5. Finally, use MCP to draft and send a personalized email to the customer summarizing the information and confirming the update (using an email tool).

This makes the AI agent far more effective and capable of resolving customer issues without human intervention, demonstrating the power of MCP in enabling AI to interact with the real world of business operations.

The Future is Connected: What's Next for MCP

The rise of protocols like MCP signals a shift in how AI will interact with the world. For AI agents to truly become capable assistants and powerful business tools, the external services, databases, and applications they need to interact with must also "speak" this new standardized language. This means that providers of these services will be responsible for implementing and maintaining their own MCP servers to expose their capabilities. This creates a compelling incentive for developers and companies offering these services to adopt MCP or similar open standards.

The future of the AI agent ecosystem is one where AI can seamlessly plug into a vast network of capabilities, leading to more intelligent automation, richer insights, and entirely new AI-powered applications we can only begin to imagine.

Until next time.
On behalf of Team Lumiera

Emma - Business Strategist
Allegra - Data Specialist

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