What is Miduconf YouTube Tools?

It's a project demonstrating how to build TypeScript-based Model Context Protocol (MCP) servers, enabling Large Language Models (LLMs) to interact with YouTube functionalities.

What YouTube capabilities does this tool provide for LLMs?

It allows LLMs to search for YouTube videos, playlists, and channels, and generate YouTube video titles using AI models via MCP sampling.

What is the Model Context Protocol (MCP)?

MCP is an open standard facilitating communication between language models and external tools, APIs, or services, enabling LLMs to interact with real-world data and functionalities in a structured and secure manner.

How does it integrate with AI chat clients like GitHub Copilot Chat?

The MCP server integrates seamlessly with clients like GitHub Copilot Chat, allowing them to discover and utilize its YouTube tools and pre-defined prompts for enhanced AI interaction.

Miduconf YouTube Tools

Name: Miduconf YouTube Tools
Author: 0GiS0

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Demonstrates how to build Model Context Protocol (MCP) servers with TypeScript, enabling large language models to interact with YouTube functionalities and custom prompts.

This repository contains the practical demonstrations from the Miduconf 2025 talk on programming Model Context Protocol (MCP) servers. MCP is an open standard enabling structured and secure communication between language models and external tools like APIs or databases. This project specifically showcases how to implement an MCP server in TypeScript, providing a suite of YouTube-related functionalities that allow large language models (such as GitHub Copilot Chat) to search videos, playlists, channels, generate video titles, and utilize pre-defined prompts or elicitations for enhanced interactions. It also illustrates how an MCP server can leverage the client's own AI models through 'sampling' for tasks like content generation.

Key Features

01TypeScript-based MCP Server implementation for demonstrating Model Context Protocol.

02Integrated YouTube tools for searching videos, playlists, and channels.

03Functionality to generate YouTube video titles using AI models via MCP sampling.

04Pre-defined prompts designed for seamless integration with LLM chat clients like GitHub Copilot Chat.

05Demonstrates advanced MCP features including elicitation for additional user input.

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Use Cases

01Extending AI chat clients (e.g., GitHub Copilot Chat) with external tools and prompts.

02Learning to develop and configure custom Model Context Protocol (MCP) servers.

03Integrating YouTube data and content creation capabilities into AI-powered workflows.