What is the Model Context Protocol?
First of all, what is actually the Model Context Protocol, or MCP as many calls it. The MCP is a way to standardize communication between Large Language Models (LLM), and external tools, services, and data sources. By standardizing this communication, you can get more consistent and reliable answers. You can almost think of the Model Context Processor as a translator for AI that makes it easier to «speak» with databases, services, and applications in your own language.
If you have any experience with AI, you know that the answers you get can be inconsistent, and not always that reliable. Imagine going to a different country, and have a conversation with someone in a completely different language. Without a translator, or a common protocol, it would be extremely difficult to have this conversation. And this is a little bit how AI has been working with different
things before MCPs was born.
The Problem MCP Solves
Before we’re diving into how MCPs are actually working, I just want to go through a little bit more about the challenges that led to the development of the Model Context Processor.
At a point, the AI language models became very sophisticated and capable. This led to many users, and developers wanting AI to do more than just generate text. The more it was used, the more we wanted things like:
• Access real-time information from the internet
• Control apps and services
• The ability to process files and documents
• Interact with databases
• Connecting to APIs and other web services
The problem with this is that for each of these types of features, the developers had to create different integrations for each of the tools or service they wanted to use together with their AI. And since so many people worked on this without any type of standardized way to do it, the solutions became very different. So many AI applications used different methods to accomplish very similar tasks.
And when there’s many ways to do the same thing, it can become a bit inconsistent and hard to rely on.
A Brief History: Why MCP Was Needed
In the early days of modern AI assistants, each interaction with external systems was essentially a custom hack, something a developer just made. If you wanted an AI to check the weather, you’d need to write specific code for a weather API. If you wanted it to search your files, you’d need another custom integration. If you wanted it to interact with a database, that required yet another approach.
This fragmentation created several problems:
Inconsistency: Different AI applications handled similar tasks in completely different ways, making it difficult for users to predict behavior across platforms.
Redundant Work: Developers were constantly reinventing the wheel, creating similar integrations repeatedly for different AI systems.
Maintenance Nightmares: Each custom integration needed to be maintained separately, updated when APIs changed, and debugged independently.
Limited Scalability: Adding new tools or services to an AI system required significant development work, limiting how quickly AI capabilities could expand.
Security Concerns: Without standardized protocols, each integration potentially introduced new security vulnerabilities.
How MCP Works: The Technical Foundation
At its core, MCP establishes a standardized way for AI models to discover, connect to, and interact with external resources. It defines a common “vocabulary” and set of rules that both AI models and external services can understand.
The protocol operates on several key principles:
Standardized Communication: MCP defines specific message formats and communication patterns that all compatible systems can understand. This means an AI model can interact with any MCP-compatible service using the same basic approach.
Resource Discovery: The protocol includes mechanisms for AI models to discover what resources and capabilities are available from connected services. It’s like having a menu that tells the AI what actions it can perform.
Secure Interactions: MCP includes built-in security features to ensure that interactions between AI models and external services are safe and authorized.
Extensibility: The protocol is designed to grow and adapt as new types of services and capabilities emerge.
The «flow» for the Model Context Protocol is something like this:
What happens here is that you use some sort of MCP client. This can be Claude, an IDE, or similar. I often use MCP’s in one of my favorite editors, Cursor.
Let’s say that I have set ut Notion as one of my MCP servers. My client then sends a request through the protocol to the MCP server. The MCP server then connects to my Notion, and does
some magic with the data. Then it returns the data back through the protocol with data in a standardized format that my client knows exactly how to display and present to me.
Real-World Applications
Okay, so how can I use MCP in my day to day life? Let’s go through some practical examples of how you can benefit from.
As a research assistant: An AI researcher could use MCP to seamlessly access academic databases, retrieve papers, analyze data from spreadsheets, and even run statistical calculations—all through natural language commands. The AI doesn’t need separate integrations for each database or tool; MCP provides a unified interface.
As a business analytics: A business analyst could ask an AI to pull sales data from a CRM system, combine it with marketing data from another platform, perform calculations, and generate reports. MCP enables the AI to interact with all these different business systems using a single, standardized approach.
For personal productivity: A user could ask their AI assistant to check their calendar, read recent emails, update a project management tool, and schedule meetings. Instead of needing separate integrations for each service, MCP allows seamless interaction across all productivity tools.
Benefits for Users and Developers
The Model Context Protocol offers significant advantages for both end users and developers:
For Users:
• More capable AI assistants that can interact with a wider range of tools and services • Consistent experience across different AI applications
• Faster integration of new capabilities as services adopt MCP
• Better reliability and security in AI interactions
For Developers:
• Reduced development time when creating AI integrations
• Standardized tools and libraries for building MCP-compatible services
• Easier maintenance and updates across multiple integrations
• Better interoperability between different AI systems and services
The Current Landscape and Future Outlook
MCP represents a significant step toward more interoperable AI systems. As more services and tools adopt the protocol, we can expect to see AI assistants become increasingly capable and versatile.
The protocol is still evolving, with ongoing work to expand its capabilities and refine its specifications. Major AI companies and service providers are already beginning to adopt MCP. This makes the potential for this to become the standard in the industry much better.
While AI has been good so far, it hasn’t been really great because the results we got was not very reliable. In the future, I hope that AI assistants can work much better for you, and work better with all digital tools and services. The MCP is here to help us with exactly this, and by adopting this protocol, AI can be even more revolutionary.
Conclusion
The MCP or Model Context Protocol now represents a crucial stage in the development of a more useful and reliable AI. It does so by providing a standardized way for AI models to interact with external tools and service.
For beginners, the key takeaway is that MCP is essentially a universal language that allows AI to communicate with various digital tools and services consistently and reliably. As this protocol becomes more widely adopted, we can expect AI assistants to become more capable, reliable, and useful in our daily lives.
The use of MCP’s also might lead to a broader use of AI because we can now rely more on the results.
To get started off on the right foot, check out the Obot MCP Gateway. Our open source platform helps IT teams bring order to the rapid growth of MCP servers, so developers and employees can securely connect AI tools to the apps and data they need. Download Obot at https://github.com/obot-platform/obot.
