How to Build (Custom) AI Agents with MCP

Shaw Talebi

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Video Summary

The video explains how to build custom AI agents using MCP (Model Connect Protocol). MCP is described as the "USBC port of AI apps," enabling AI applications to connect with various tools and context. This allows for custom integrations, such as giving an AI access to Google Drive and Slack, and facilitates plug-and-play toolsets for LLMs to build custom applications and agents. The MCP architecture follows a client-server model, where clients send requests for tools or resources to servers, which then respond with the requested information or execute tasks.

The core components of MCP are the client and the server. The client, embedded in an AI application, handles discovering server capabilities, receiving data, and managing tool execution. The server, in turn, hosts a set of tools (like Python functions or API access), resources (databases, file systems), and pre-written prompts to assist the AI. Communication between clients and servers can occur locally via standard IO or remotely using HTTP.

A practical demonstration shows building a YouTube agent by creating an MCP server with tools for fetching transcripts and instructions. This server is then connected to an LLM using OpenAI's agents SDK, allowing the agent to extract transcripts, generate chapter timestamps and titles, and create blog or social media posts based on YouTube video content. The agent can be interacted with via a command-line interface, showcasing its ability to process video links and generate structured content.

Short Highlights

MCP acts as a universal connector for AI applications, akin to USB-C for devices.
It enables AI apps to integrate external tools and data sources like Google Drive or Slack.
The MCP architecture uses a client-server model for communication.
An MCP server can host tools, resources, and prompts.
A custom YouTube agent was built using MCP and OpenAI's SDK to process video transcripts and generate content.

Key Details

Understanding MCP: The "USBC Port of AI Apps" [1:30]

MCP (Model Connect Protocol) is a standard method for connecting tools and context to AI applications.
It is compared to the USBC port, allowing AI apps to plug in various peripheral devices (tools and context).
Concrete use cases include custom integrations for AI apps like Claude Desktop, granting access to Google Drive and Slack for context and actions (messaging, summaries).
MCP allows for plugging and playing different toolsets and building custom AI agents.