AI Blog: Model Context Protocol (MCP) vs. Traditional Application Programming Interface (API)
8 August 2025
Welcome back to our AI blog series! Today, we’re diving into a comparison of a Model Context Protocol (MCP) with a traditional Application Programming Interface (API) and discuss why an MCP represents a significant paradigm shift for AI-driven applications.
Understanding Traditional APIs
An Application Programming Interface (API) is a set of defined rules and protocols that allows different software applications to communicate with each other. They act as intermediaries, enabling one system to request data or functionality from another. Most commonly, traditional APIs are RESTful, relying on HTTP to define specific endpoints.
For those that don’t know, REST is a software architectural style that was created to describe the design, and guide the development of the architecture for the World Wide Web. REST defines a set of constraints for how the architecture of a distributed, internet-scale hypermedia system, such as the web, should behave. Therefore, a RESTful API is a way for computer systems to exchange information over the internet.
How Traditional APIs Function
What is an endpoint? An endpoint is a specific URL (web address) where an API can access the resources provided. Each endpoint corresponds to a particular function or data item, such as retrieving a list of products or submitting a new order. This means developers must have prior knowledge of the exact endpoints and the methods to use when passing them on to the AI.


Why Traditional APIs Struggle with Modern AI Needs
While traditional APIs have been essential in software development, they don’t work well with today’s flexible and fast-moving AI systems for the following reasons:
- Too rigid: traditional APIs are fixed and hard to update. If you add new features, you often need to create new versions (like /v1/orders vs /v2/orders), which can break older systems and create lots of extra work
- Hard to understand: API documentation is often outdated or missing. Developers waste time trying to figure out how to use the API correctly
- Not made for AI: traditional APIs expect set rules and endpoints, but AI tools need to interact more freely and adapt in real time
- Takes too much effort to connect: each API integration means separate code, documentation, authentication methods, error handling and maintenance. Using multiple APIs means writing different code for each, handling different login methods and dealing with different errors.
Key Advantages of MCP
MCP offers several compelling advantages over traditional APIs, particularly for AI-driven applications:
- Dynamic Adaptability: MCP allows the modification of a tool’s parameters without breaking clients. Clients can dynamically adapt to the updated tool description. New tools are automatically discoverable by clients. This dynamic discovery is a cornerstone of MCP’s power.
- Context Awareness: MCP allows tools to be exposed based on context (e.g. a “send message” tool is only accessible for logged-in clients). This adds a layer of flexibility and security that is not easily achievable with traditional APIs.
- Real-Time, Two-Way Communication: MCP supports persistent, real-time two-way communication, allowing AI models to not only retrieve information but also to dynamically trigger actions, which is crucial for complex and interactive AI workflows.
- Simplified Integration: MCP makes it much easier to connect AI tools to other systems. You don’t need to write lots of custom code, making the whole development process faster and simpler.
- Scalability: MCP can grow and change without the versioning issues that often come with APIs. It’s built to scale easily, so you can update or expand one part of the system without messing up everything else.

In conclusion, MCP helps to address the limitations of traditional APIs by providing a standardised, self-describing and dynamically adaptable framework for AI integration.
MCP is not just a great improvement. It represents a fundamental rethinking of how AI systems interact with external tools and data. As AI continues to evolve and become more useful in our daily lives, even people without any programming background can build their own AI tools with its help.
It’s like being able to lift yourself off the ground by grabbing your own collar with one hand. This is the true magic of AI — something that once seemed impossible is now within easy reach.
Join us next time for more exciting news on AI tools!