Detailed info and comparison of LLm vs MCP

When comparing LLMs (Large Language Models) and MCP (Model Context Protocol), it's crucial to understand that they serve distinct but complementary roles in the ecosystem of AI and language processing. Here's a breakdown:

LLMs (Large Language Models):

• Core Function:
  • LLMs are AI models designed to understand and generate human-like text. They excel at tasks like:
    • Natural language understanding and generation.
    • Text summarization, translation, and question answering.
    • Generating code and creative content.
• Functionality:
  • LLMs process vast amounts of text data to learn patterns and relationships, enabling them to generate coherent and contextually relevant responses.
  • A key feature that is very relevant to the MCP discussion, is "function calling". This is the ability of an LLM to recognize when a user query would be best served by an external tool, and then to format a request that that tool can understand.
• Limitations:
  • LLMs themselves don't inherently interact with external systems or databases. They need mechanisms to connect with the real world.
  • The way that "function calling" is implemented can vary from LLM provider to LLM provider, creating interoperability issues.

MCP (Model Context Protocol):

• Core Function:
  • MCP is a protocol that standardizes how LLMs interact with external tools and services. It focuses on the execution of actions that LLMs initiate.
  • Essentially, it provides a consistent framework for LLMs to access and utilize external capabilities.
• Functionality:
  • MCP enables interoperability by defining a standardized way for LLMs to communicate with various tools, regardless of their underlying technology.
  • It facilitates scalability by allowing LLMs to seamlessly integrate with a wide range of applications and services.
  • MCP helps to create a more consistent and reliable way for LLMs to interact with external tools.
• Relationship to Function Calling:
  • LLM "function calling" generates the instructions for an external tool. MCP provides the standardized method for those instructions to be executed.
  • Therefore, function calling and MCP work in tandem. Function calling creates the "what needs to be done" and MCP allows for the "how it is done" in a standardized way.

Key Comparisons:

• Purpose:
  • LLMs: Focus on understanding and generating language.
  • MCP: Focus on standardizing the execution of LLM-generated actions.
• Scope:
  • LLMs: Operate within the realm of language processing.
  • MCP: Operates within the realm of system integration and interoperability.
• Interoperability:
  • LLM function calling is not standardized across platforms.
  • MCP provides a standardized protocol to increase interoperability.

In essence, LLMs are the brains, and MCP is the standardized communication network that enables those brains to interact with the world.