One of the most significant developments in the evolution of AI systems, particularly large language models (LLMs), is the introduction of the Model Context Protocol (MCP).
Designed as an open standard and supported by an open-source framework, MCP streamlines the way AI models interface with external tools, systems, and data sources. Rather than treating these elements as isolated or inaccessible, MCP provides a universal method for reading files, executing functions, and managing contextual inputs.
According to Wikipedia, MCP essentially acts as a bridge between the model and its operational environment, allowing it to extend its capabilities beyond pre-trained knowledge.
This makes it possible for AI to not just respond intelligently, but to take meaningful action, interacting with live data, running code, or adapting dynamically to new contexts, which marks a major step toward more autonomous and practical AI applications.
Building and using an MCP
All of the above is useless unless you know how to apply it to your business.
In the article link below, we will look at:
- Defining an MCP
- How to begin
- Who can help
- How to devise a plan
Please follow the link below and learn more.
