Introduction
Imagine the early days of the internet, when computers were like isolated islands, each speaking its own language. Then came HTTP (Hypertext Transfer Protocol) in the early 1990s. These commands bridged the gap between these islands, allowing them to share information seamlessly. This foundational protocol allowed users to retrieve and send data across the World Wide Web. This laid the groundwork for the interconnected digital world we know today.
As the web evolved, developers sought more efficient ways for applications to communicate. This led to the birth of REST (Representational State Transfer) in the early 2000s. REST standardized the approach for building web services, emphasizing simplicity and scalability. It allowed different software systems to interact over the internet using a common set of principles. It made integrations more straightforward, paving the way for the dynamic, data-driven applications that have become integral to our daily lives.
Fast forward to today, and we’re witnessing another transformative shift with the rise of artificial intelligence. AI agents are becoming ubiquitous but operating in silos, unable to tap into the vast reservoirs of data housed in various applications and services.
Enter the Model Context Protocol (MCP). Just as USB-C provides a universal connector for devices, MCP offers a standardized way for AI models to connect with diverse data sources and tools. This open standard enables AI assistants to access real-time, relevant information, enhancing their capabilities and ensuring they provide more accurate and context-aware responses.
Why MCP?
LLMs are amazing at understanding and writing text. However, all they do is predict the next word. They can write emails but can’t send them. In our existing digital ecosystem, we have systems (tools) that can perform actions (e.g., Email management systems to send emails).
LLMs can instruct email management systems to send emails. However, with more LLM apps in use, coordinating their external tools such as databases, APIs, and other systems become increasingly challenging.
Currently, many applications use their own custom methods to communicate with tools. This fragmented approach is inefficient and hard to maintain and scale. Imagine if every computer in a network had to use a completely different communication method to talk to every other machine. The result would be confusion, inefficiency, and constant failure as systems simply would not work together.
Anthropic proposed a promising open standard called MCP (Model Context Protocol), which is a game-changer for how language models interact with external tools. With MCP, developers can define a common interface for tool communication, bringing much-needed order to today’s diverse and often inconsistent methods.
Think of it like the introduction of REST for the web. Before REST, every web service worked in its own way, making integration complex. REST brought in a simple, standardized way for clients and servers to talk and transformed the web. MCP aims to standardize AI ecosystems in the same way.
In early 2025, all the major AI industry players rallied behind MCP as “the” communication protocol for an Agentic world. Amazon, Google, Microsoft, and, of course, OpenAI publicly announce their native support for MCP.
Now that it is widely adopted, MCP will enable new clients to interact with tools consistently, and tool servers will define roles, access levels, and capabilities within a unified structure. It’s a step toward building a more scalable, secure, and interoperable AI infrastructure.
MCP Architecture
The Model Context Protocol (MCP) is designed to facilitate seamless communication between AI applications and external data sources. Its architecture comprises the following key components:
- MCP Clients: AI-powered applications, such as chatbots or integrated development environment (IDE) assistants, that require access to external data or functionalities. They request MCP Servers to retrieve information or perform specific tasks.
- MCP Servers: Servers that act as intermediaries, exposing data sources or services to MCP Clients. They process incoming requests, interact with the necessary data repositories or tools, and return the appropriate responses to the clients.
- Data Sources and Services: Various repositories, databases, or external tools that house the information or capabilities the MCP Clients seek to access. MCP Servers interface with these sources to fulfill client requests.
Example: Reimbursement Workflow
Let’s take the example of an employee reimbursement workflow. Every employee accesses this process through the company’s internal employee reimbursement portal. There, users submit claims by uploading documents. Finally, the finance team reviews and either approves or rejects those claims.
In a process like this one, MCP allows AI agents to communicate with the backend of the employee reimbursement portal using natural language. Similarly, other relevant systems and software can also be made accessible to this expense report Agent through their respective MCP servers.
In this case, the agent can perform various actions, such as:
- Analyzing the claim and processing the documents submitted by the employee
- Calling an OCR tool to extract the receipts numerical values and an internal policy document to validate whether the receipt aligns with the policy.
- Reviewing the claim request and approving or rejecting it. For example, the agent can send a command through MCP like “Reject claim #EM9017” and notify the employee that they submitted the wrong type of receipt.
Security
The Model Context Protocol (MCP) not only standardizes communication but also embeds safeguards to ensure only the right agents access the right tools. Before making any request, the MCP server first checks:
- Authentication: Is this agent who it claims to be?
- Authorization: Does this agent have permission to access this specific tool?
These checks are built into the core workflow, ensuring secure and responsible agent-tool interactions. While this aspect of MCP is still evolving, we expect robust open-source implementations and security frameworks to emerge soon, making it easier for organizations to build safe and scalable agentic ecosystems.
Getting started with MCP
Agentic AI can transform the way businesses operate, from rethinking workflows to fully automating complex processes. However, most enterprises are still held back by disconnected systems, legacy software, and scattered integrations. Here is a three-step approach to help enterprises get started:
- Assess and Integrate: Start by evaluating open-source MCP implementations and identifying where they fit into your current architecture.
- Build: Create custom MCP servers that connect internal tools, data sources, and services to agentic systems.
- Secure: Design strong security and governance controls to manage how AI agents access and use enterprise tools.
By putting these building blocks in place, enterprises can unlock faster, smarter decision-making powered by secure and scalable agentic AI.
Conclusion
The internet evolved through protocols like HTTP and REST, which standardized how devices share data. Today, AI agents face a similar challenge: siloed tools and fragmented communication. The Model Context Protocol (MCP) solves this by acting as a universal bridge. It enables AI systems to securely interact with databases, APIs, and enterprise tools using natural language, just as REST streamlined web services.
MCP’s authentication and authorization safeguards ensure trust, while its open framework simplifies scaling. With native MCP support coming to platforms like Cogentiq, businesses can accelerate decision-making, enabling faster, smarter actions across complex workflows.
