Exploring the Future of MCP: Unleashing the Power of Local Models and Intelligent Agents

In today's fast-paced world of artificial intelligence, Model Context Protocol (MCP) stands out as a transformative force. It is not just another tech trend; it's a way to make our interactions with machines more intuitive and effective. With an influx of ideas, let's explore how MCP utilizes local models, smart coding agents, and collaborative frameworks to revolutionize our technological landscape.

Understanding MCP

MCP is a cutting-edge framework that allows users to interact with AI using natural language. This capability triggers a set of tools designed to perform tasks across various platforms with greater efficiency. At its heart, MCP aims to enhance productivity by improving communication between diverse tools, enabling smoother processes in tasks ranging from managing emails to coding.

Keep it simple

Think of it like this - right now you are interacting with a website. You click buttons and they do things. This is called an HTTP request - in lay terms your button clicks instruct the backend databases and code to "do stuff". The reason the internet works is because there is a standard way of handling these interactions, meaning developers can use the same tools to drive all web pages or back-end APIs.

For a little while now we have had the ability to chat to our familiar LLMs like ChatGPT, and we get a response, invariably couched in friendly language and is accurate to a varying degree.

But what if instead of just getting a text response, you could ask you LLM to actually do things. So yes, for the Star Trek fans, this is now possible....

The real magic of MCP comes from its ability to incorporate third-party tools and use locally created models tailored for specific tasks. Think about having an AI that not only interprets your commands but also accesses your own documents for personalized results and performs actions using your software like emails. This encapsulates the essence of MCP—a seamless connection between human intent and machine capability.

The "protocol" in MCP is really like a "translator".

We ask LLM's in human language, but that means nothing to specific python code. But MCP enables the AI to direct your request to perform the actions in the code.

You might, say, "send an email to Bob telling him we can meet for coffee tomorrow at 11am." That triggers a call to your email (which is already authenticated (meaning secure) and given powers to write and send emails). Similarly, we might ask the LLM to take a look at a file or document and give us an opinion or suggestions for improvement.

In that instance, the code is executed but it then has to be interpreted back into something human's can understand.

So MCP's represent a fantastic new tool box, which you can create yourself (ironically made easy as it's aided by LLMs). There is a growing community of existing MCPs out there, but it is completely unregulated and we are just beginning to see some kind of standardisation from the big players like Anthropic and ChatGPT.

Programming code representing AI's interaction with user tasks and queries.

Excited and cautious

As a general technology enthusiast, this is very appealing. You can tune your MCP script (which is all it is under the hood) very specifically on the task it performs, but leverage from AI to direct the traffic for you. Best of both worlds.

So, why cautious?

MCP's can be hosted locally i.e. on your own machine. So the toolbox itself and the files it connects to can be completely local - great!

But wait, what about the LLM? Here's the problem - the really smart LLM's - the ones you probably use every day are massive.

Most are built with very broad capabilities for coding, reasoning and general conversation. So they have to juggle many billions of lines of text and do it's vectorisation and embedding witchcraft. Consequently, most people using MCPs are using these massive LLMs over the web, so they can leverage from the huge servers running the leading edge NVIDIA GPUs you may have heard of. But of course, that means even with your local MCP toolbox, you have to send your data over to a remote LLM server, which gets the IT security guys twitching (not to mention regulators if you are in a sensitive industry like Healthcare or Financial Services).

Now, you can host LLMs locally on your laptop or desktop. Services like Ollama can be downloaded very easily onto your desktop, which are basically a way to connect to a massive library of LLMs and pull them down to your desktop too.

But I guarantee that your laptop won't be highly spec'd enough to handle the models you really need. Use a smaller model? Sure, but then you sacrifice the size of "brain" running the show.

By using local models, privacy is maintained, and processing speed is increased since tasks are often handled directly on the user's device instead of relying on the cloud. This feature is crucial in scenarios that require confidentiality, such as legal or medical tasks.

Small-Scale Local Models for Specific Tasks

Sometimes, large AI models are excessive for niche tasks. This is where small-scale local models excel. By focusing on particular applications, these models provide quicker and more efficient solutions.

For example, if you're creating a basic web application, a small local model could efficiently tackle special coding challenges, such as user interface design or database queries, without the burden of a more complex setup. This agility allows developers to address specific problems swiftly and effectively.

Combined with MCP, small-scale models can interact with larger systems, delivering both speed and functionality for the user.

The Vision: Multi-Agent Frameworks

Imagine a more ambitious use of MCP as a multi-agent framework where various agents collaborate towards a shared objective. The goal is to leverage MCP to manage these specialized agents effectively.

In this model, a master "agent" (meaning an LLM) would supervise the activities of smaller, specialized agents, ensuring they work together smoothly. Visualizing the connections between these agents can enhance understanding and improve task management. The "glue" enabling the translation of all the technical gobbledegook back-and-forth is MCP.

This intricate network facilitates seamless transitions as agents handle complex projects, whether managing software development cycles or undertaking large data analyses. The potential gains in efficiency are enormous since each agent can apply its unique skills while remaining coordinated through MCP.

Enhancing MCP Capabilities

The learning process does not stop here. Continuously improving your MCP code is vital for staying up-to-date with AI developments. Regular updates not only ensure your tools are relevant but also facilitate the integration of new features as they arise.

Joining forums or communities focused on MCP and related technologies can be immensely beneficial. Developers can share insights, learn from real-world experiences, and contribute to a growing knowledge base that enhances the collective understanding.

Investing time in enhancing your MCP code can greatly boost project management and significantly improve their effectiveness.

Looking Ahead

As we consider the future of MCP, the opportunity for growth and innovation is vast. By harnessing local models, collaborating with intelligent coding agents, and developing efficient applications, MCP is reshaping the potential of artificial intelligence.

This powerful technology not only increases productivity but also enhances our experiences by allowing us to focus on creativity and strategic thinking rather than routine tasks. In a time where ideas are abundant, employing the capabilities of MCP could be the key to transforming those ideas into tangible outcomes, paving the way for a future where intelligent agents synchronize with us to fulfil our aspirations.