This video explains the inefficiencies and problems associated with the traditional Model Context Protocol (MCP) when building AI agents. It introduces a new approach, referred to as MCP 2.0 or code execution, which significantly improves token efficiency, reduces costs, enhances reliability, and offers better privacy by allowing AI models to write code to interact with tools rather than directly calling functions. The video contrasts the two methods, highlights real-world applications, and discusses when to use each approach.
The main problems encountered when using the traditional Model Context Protocol (MCP) are:
The code execution approach (MCP 2.0) improves token efficiency and reduces costs in several ways:
Here are the key differences in how AI agents interact with tools in the traditional MCP versus the code execution method:
Traditional MCP:
Code Execution Method (MCP 2.0):
The code execution approach, while powerful, has some downsides and limitations: