Making Sense of LLM Tool Use & MCP



AI Summary

Summary of MCP (Model Context Protocol) Video

Introduction

  • MCP is an AI-related buzzword that has gained attention recently.
  • The creator explored MCP and attempted to build an MCP server during a livestream.
  • An article and examples of an MCP server and client are provided in the video description.

Understanding LLMs and AI Applications

  • LLMs (Large Language Models) are essentially text generators that produce tokens (words or parts of words).
  • Despite advancements, LLMs are not all-powerful and are limited to generating text.
  • AI applications like ChatGPT use LLMs within an application shell, which includes additional code for functionality such as web search or running Python code.

How AI Applications Use Tools

  • AI applications can use tools by injecting system prompts into the LLM, defining guardrails, and listing available tools.
  • When a user asks a question, the LLM generates tokens that may describe the use of a tool, like a web search.
  • The application checks the LLM’s output and executes the tool use behind the scenes, enriching the chat history with the results.
  • The final result, based on the enriched chat history, is then shown to the user.

Building AI Applications with Tool Use

  • Developers can build AI applications using APIs like OpenAI’s and create system prompts for tool descriptions.
  • OpenAI’s API has a functions feature to simplify tool exposure to the LLM.

Model Context Protocol (MCP)

  • MCP standardizes the description and use of tools for LLMs.
  • Developers can build MCP servers for their applications, using official SDKs to describe tools in a standardized way.
  • MCP clients, AI applications using LLMs, can communicate with MCP servers to use these tools without manual setup.
  • MCP servers wrap the logic for APIs, making it easier to define, share, and use tools in a standardized manner.

MCP vs. Traditional APIs

  • MCP is seen by some as a buzzword, equating it to traditional APIs.
  • The difference lies in the ease of exposing and using tools, not in the fundamental capability to connect to APIs.

Future of MCP

  • There is already a list of MCP servers available for installation into MCP-enabled applications.
  • Users can install extra tools into AI applications to enhance capabilities.
  • MCP servers can be pre-installed in AI chatbots for added functionality.

Conclusion

  • The creator believes MCP is not just hype but a useful development in AI tool integration.
  • Comments and feedback are encouraged.

Notes

  • No detailed instructions such as CLI commands, website URLs, or tips were provided in the transcript.
  • Self-promotion from the author was excluded as per instructions.