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.