How Does MCP Help AI Application Development?
Technology is great! But it can be hard to keep up. Even if you have made a career in technology, the pace of change today is so rapid that, if you miss one issue of your favorite tech publication, you may risk falling behind.
Perhaps nothing has increased the pace of change in technology more than Artificial Intelligence (AI) and, because AI potential seems to be unconstrained, the need for expanded capabilities and foundations is constant.
One such development is Model Context Protocol (MCP). MCP is an open protocol that was created by Anthropic to simplify the process of interacting with Large Language Models (LLMs) and to standardize the way in which applications provide context to LLMs and help them interpret data.
It may help to think of MCP as a translator or a way to make connections. Much like a USB adapter can connect an external hard drive to your laptop, MCP can connect various tools and data sources to enable interaction, integration and context for use in AI.
While early stage AI struggled to connect disparate data sources, tools and Application Programming Interfaces (APIs), the advent of Model Context Protocol (MCP) provides a bridge to external data and services to connect AI models using a standardized communication framework to allow for AI reasoning and processing. So, AI models like Azure OpenAI, GPT, Atlassian and others can fetch data, connect and interact with APIs and perform tasks, going well beyond the knowledge contained in the model to produce new, expanded outputs.
In the good old days of AI (just last year), your users might ask a complex question or a question that exceeded the information contained within an LLM training data set. That question could elicit an answer that made no sense or the system might simply frustrate the user by saying, ‘I don’t know.’ In order to solve that problem, you would have to provide data refinement to ensure that the LLM had context or you would have to add another tool or secondary source. That can be complex, time consuming and expensive.
In short, in order to succeed with your LLM, you were constrained by the amount of training data, and how well you could anticipate what your clients or users would ask or need. Sure, the information exists out there somewhere, but your LLM doesn’t include that data! You could use APIs but that process of application integration is complex and can be difficult to implement in a meaningful way, and you have to hard-code each connection! Using this technique to provide information to an LLM requires you to review documentation and data, identify the end point of the search, verify authentication, structure requests and then make sure it all works seamlessly so your users are not frustrated.
MCP allows you to create a bridge between apps and tools and establish automated workflows, using the power of LLMs to perform tasks and provide clear, concise information across all technology frameworks and platforms. MCP allows developers and content managers to establish what the LLM should know and provide that in a standard format that the LLM can understand. In essence, MCP acts as the go-between or the middleman, simplifying the relationship and connection between the LLM and APIs, tools and data repositories. Rather than your app reaching out to the API, it communicates with an MCP server. The MCP server will then translate that information and decide how to communicate with the API to satisfy the user request. It’s a translator!
Model Context Protocols (MCP) provide support for application developers using AI so they can more easily build apps and integrate information, ensuring that the app is flexible enough to support future integration of tools and data. Its open-source accessibility allows software developers and software vendors to leverage these tools to create business and consumer apps.
The team can create apps that are extensible at runtime and connect tools and APIs to an MCP server, to use the app immediately without extensive coding and deployment. The process is simple.
- When a user enters a query, the Large Language Model (LLM) sends a request to the MCP server
- The MCP server translates the request and decides where it should go (API, tool, etc.), and then sends it to the appropriate source
- The response to the query is returned through the MCP server
- The MCP server sends that response to the LLM
- The user receives the response
It’s just that simple.
If you, your IT staff, your management team or your customers are asking about the potential of AI and LLMs, it is time to consider MCP and how it can support your needs. The incorporation of this approach can save development time and expense and alleviate rework and developer and user frustration.
If your business wishes to improve productivity, timelines, budgets and dependability of in-house applications, you will want to find a vendor and service provider who appropriately employs AI and LLMs to support its development model. If you are planning to engage an IT expert to augment your own software product or solution, it is wise to look for this capability when you interview prospective partners. Contact Us to find out how to integrate AI And LLM capabilities into your software project, website, analytics initiative or other project. Explore our free White Papers: ‘What Is AI And How Can It Help My Business,’ and ‘The Practical Use Of GenAI In BI And Analytics Tools.’
