Model Context Protocols: The next evolutionary leap in AI - for real
AI tools are now a friend and helper to us all and have become an integral part of our everyday working lives. With the right prompts, they are now the right hand for many and relieve us of repetitive and time-consuming tasks.
Following in the footsteps of AI tools, AI agents have also developed rapidly. As autonomous entities, these agents proactively take on tasks, automate and optimize workflows and even make decisions for us. In other words, more smart colleagues than just a right-hand man.
But the AI journey continues. And the next big step is called Model Context Protocols. Sounds technical at first - but it's a real game changer.
Model Context - what?
The Model Context Protocol (MCP for short) is an open standard that shares context seamlessly between AI systems - for smarter, more consistent and collaborative AI experiences.
In other words, it provides AI tools with tools that give them greater context so they can work even more efficiently. Through MCPs, AI tools can also be linked together, gain access to other data points, collaborate across platforms - and thus continue to revolutionize our (working) world.
And what are the benefits - for example in Contentful?
Let's take a look at the whole thing using an example in content management: Let's take Contentful. An MCP that is tailored to this platform can be fed directly with instructions - and thus provide massive support for content maintenance. Because it not only knows the content types in a system - it also knows the right way to get the necessary information to use it for a page. For example, using the "get_content_type" tool.
The highlight: as soon as the AI understands the context, it can get started on its own. It no longer asks questions at every step, but creates complete pages in Contentful based on the data and input provided. Independently - and efficiently.
This means that as a content manager, I no longer have to create the page block by block, content type by content type. I can prompt the content directly into the MCP without time-consuming briefings - and the rest is done as if by magic.
How does an MCP work?
There is a system behind the magic: the MCP extends the understanding of the AI model used by providing access to data sources and external services - and putting them into context.
What sounds abstract is illustrated quite well using the example of a salad bowl. Metaphorically, the bowl here is the shared context provided by the AI. The ingredients are the individual tools and data sources. The dressing is made up of the predefined rules and protocols that dictate how the ingredients work together and interact. Tadaa: well mixed, the salad is ready to be served to the user.
Four reasons why MCPs is "Your AI but better"
What is the advantage of Model Context Protocols compared to the standard use of ChatGPT and co?
The context is predefined in a script and does not have to be inserted anew in every prompt. This ensures increased precision, uniformity and time savings
Workflows become smarter and faster. Everything flows together in a single system.
Individual silos are connected. By combining data sources, services and artificial intelligence, we say goodbye to separate silos and have a single point of truth.
The context model can be adapted to any use case. We are no longer restricted by the limitations of individual models and systems - instead, we can adapt the model to each individual requirement.
Wrap Up: MCP = the real MVP
AI workflows have made us more productive - and MCP workflows are now making our AI workflows more productive.
Model Context Protocols are still relatively new and currently thrive on the innovative tinkering of curious minds.
If you get in early, you can not only shine as an early adopter, but also create a real head start in everyday life: through seamlessly integrated AI that understands, thinks and delivers. Just like a real MVP.
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