Co-founder and CEO at 10 Senses
These days, the Artificial Intelligence (AI) world is evolving rapidly and, as a result, more and more AI agents appear in our everyday lives.
Nevertheless, these AI models are still struggling with a fundamental challenge. This is how to effectively interact with a wide range of external resources and tools.
Here comes the Model Context Protocol (MCP). It promises to revolutionize AI interaction, integrate AI models with various data systems, and lay the foundation for more autonomous AI assistants.
Once you read this article, you will know:
- where the Model Context Protocol comes from,
- what is the Model Context Protocol definition,
- what is the Model Context Protocol architecture,
- where you can use and where you should not use the Model Context Protocol,
- what are the benefits of the Model Context Protocol?
Where the Model Context Protocol comes from
In fact, before MCP, the integration of AI models with external functionalities was a huge peeve for AI developers and required a lot of manual work.
For example, let’s think about an app that gets real-time stock data, processes payments, translates text using external services, sends emails, updates CRM records, transforms data, and handles errors. Each of these actions requires a custom API connection.
Consequently, AI developers had to rely on extensive manual work. It resulted in entangled systems that were difficult to manage, modify, or scale.
Along with advancements of AI technologies and AI app development, companies tried to address these issues. As a result, there appeared solutions that partially resolved the problem. These were, for example:
- standardized plugin interfaces like Microsoft Semantic Kernel Plugins, OpenAI ChatGPT Plugins,
- open-source AI frameworks like LangChain, LlamaIndex, or Flowise AI.
Such tools facilitated AI models to securely connect with external tools by using a consistent format. Unfortunately, although they simplified certain aspects, they also created isolated systems and still required significant manual integration and maintenance.
Finally, in late 2024, Anthropic, an American AI safety and research company, introduced a brand-new Model Context Protocol, which promises to combat these issues.
What is the Model Context Protocol definition
In brief, MCP is a growing open-source, flexible framework where AI assistants can autonomously discover, select, authenticate, and orchestrate tools based on the task context in a secure and consistent way.
By providing users with a unified interface, it allows them to build reusable plugins and workflows that can be accessed by various AI agents. As a result, it standardizes how AI assistants interact with external tools, data sources, and services.
Such standardization lays the foundations for bringing AI tools and platforms together, accelerating innovation in the AI landscape and, finally, becoming a foundational standard in the future.
What is the Model Context Protocol architecture
The MCP architecture is built upon three basic elements that collaborate to enable secure operations between AI agents, external tools, and data sources. These are:
- MCP host,
- MCP client,
- MCP server.
MCP host
Model Context Protocol host is an AI app that provides the environment for executing AI tasks and running MCP clients. What is more, here an AI model resides and makes decisions about using tools, which often involves bringing together user interfaces for interaction and feedback.
MCP client
The Model Context Protocol client is an intermediary that manages communication between the host and MCP servers. It sends out requests, explores available functionalities, checks server capabilities, and handles live updates on task execution.
Here, the crucial task is also to maintain a tool registry. It is a list of all available MCP servers and the tools that enable dynamic discovery and selection by the AI model.
MCP server
Last but not least, the Model Context Protocol server is a powerhouse that enables access to external systems and operations. It offers core capabilities such as:
- tools to invoke external services and APIs;
- resources to provide access to datasets from various sources;
- prompts that are reusable templates and workflows that optimize AI responses and streamline repetitive tasks.
The MCP client and server communicate through a secure, two-way channel that enables smooth, real-time interactions and efficient data sharing.
This exchange relies on a structured protocol format, which defines specific message types for initiating actions (like tool/invoke), returning results (such as tool/result), and sending updates (for example, server/statusChange). This ensures consistent and organized communication between components.
Where you can use the Model Context Protocol
Truth be told, although the protocol was introduced a few months ago, numerous companies are already using it to power practical, real-world AI features that go far beyond simple chat responses.
Use cases for industry leaders
Let’s start with AI industry leaders that have quickly noticed the potential of the protocol and adopted it accordingly:
- Anthropic, the creator of MCP, uses it to enable its Claude models to interact with tools more reliably and intelligently, driving the evolution of the protocol;
- OpenAI is integrating MCP into its Agent SDK (Software Development Kit) and ChatGPT desktop apps, allowing AI assistants to connect to remote servers for tasks like scheduling and project management;
- Cursor uses MCP to power AI coding assistants inside its IDE (Integrated Development Environment), automating tasks like code analysis, testing, and refactoring;
- Other major platforms like Stripe, JetBrains, or Replit are adopting MCP to enhance agent capabilities, improve security, and streamline workflows.
Nevertheless, MCP can also be effectively implemented in other industries, outside the AI industry.
Common MCP use cases
Model Context Protocol examples in more common scenarios are:
- customer support companies and departments where AI agents can seamlessly connect with helpdesk platforms to automatically respond to tickets, assist users, and streamline support operations;
- business tools within companies to assist in updating CRMs, scheduling meetings, managing tasks, and coordinating across teams;
- developer tools to test APIs, analyze and refactor code, and even automate complex software builds by interacting directly with development environments;
- cloud services to scale AI integrations with built-in authentication and remote access in companies like Cloudflare.
The above is just the tip of the iceberg of the potential of MCP for common use that is still growing.
Where you should not use Model Context Protocol
Nonetheless, there are also areas that don’t necessarily need the implementation of the protocol. These are:
- simple isolated AI tasks like summarizing texts or answering questions without external data;
- highly specialized integrations with tools that don’t support OpenAPI specs or require custom coupled integrations;
- offline disconnected systems;
- real-time, ultra-low-latency systems, for example, high-frequency trading, as MCP introduces certain levels of latency;
- projects with minimal infrastructure, like a lightweight prototype or MVP without server hosting or authentication needs.
Summing up, MCP is a powerful tool for enabling AI to interact with external systems. Nonetheless, it’s best suited for more complex applications, as using it in simple projects might add unnecessary complexity.
What are the benefits of the Model Context Protocol
As already mentioned, MCP is a powerful technology that can revolutionize the AI landscape. To some extent we have already covered its potential, but let’s outline the key advantages of the protocol:
- access to standardized tools, making integration easier and more reliable;
- cross-platform compatibility – plugins built with MCP can work across different AI platforms, reducing duplication and speeding up development;
- security and scalability – with support for OAuth and remote server hosting, MCP ensures secure access to tools and can scale with enterprise needs;
- modularity and reusability – developers can build plugins once and reuse them in multiple apps or environments;
- community-driven ecosystem – platforms like MCP.so and Glama host thousands of plugins, making it easy to discover and share tools;
- rich developer tooling- official SDKs and community tools simplify plugin creation, lowering the barrier for new developers;
- enhanced AI capabilities by handling tasks like scheduling, coding, data analysis, and more.
Summing up, Model Context Protocol comes with a wide range of benefits both for developers, the AI community, and companies that implement complex AI models.
All in all, MCP is a powerful framework with immense potential for shaping the future of AI development. Its ability to connect AI agents with tools and APIs makes it a cornerstone for building intelligent and reliable assistants.
As its ecosystem grows, with reusable plugins, cross-platform support, and robust SDKs, MCP is set to accelerate innovation across industries. Looking ahead, MCP will be essential in transforming AI from passive chatbots into proactive collaborators that drive real-world impact.
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