Model Context Protocol | MCP explained | mcp in ai | AI mcp server
What is MCP (Model Context Protocol) - In this video I simply explain MCP protocol with Example and demos showing How mcp works, how to connect MCP servers with AI also explaining the MCP architecture.
After watching this MCP tutorial you will have complete understanding of What is MCP and how it works?
List of all available MCP servers: https://github.com/modelcontextprotocol/servers
Postgres Database MCP Server: https://github.com/modelcontextprotocol/servers/tree/main/src/postgres
Kubernetes MCP server: https://github.com/Flux159/mcp-server-kubernetes
GitHub MCP server: https://github.com/modelcontextprotocol/servers/tree/main/src/github
#mcp
Timestamp:
Don't sleep on this AI trend 0:00
Understanding the Problem MCP solves 01:02
What is MCP Protocol 03:00
How to add MCP servers to your LLMs 04:05
Github MCP Server 04:25
{Demo} Creating a repository using VS code + Github MCP server 04:41
USB‑C for AI applications 05:49
Understanding MCP architecture 06:22
MCP Server Components 07:03
{Demo} Manage Kubernetes with Kubernetes MCP server 08:27
MCP Official documentation: https://modelcontextprotocol.io/introduction
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Model Context Protocol (MCP) is an open standard driven by Anthropic that provides a uniform interface for large language model applications to connect and interact with external data sources and tools.
The protocol is designed to overcome the limitations of LLMs relying solely on their training data by enabling them to dynamically fetch relevant context and perform operations when needed.
MCP employs a client‑host‑server architecture in which an LLM host runs one or more MCP Clients that communicate with MCP Servers exposing resources, prompts, and tools.
All messages in MCP use the JSON‑RPC 2.0 format, and they can be transported over standard input/output for local integrations or over HTTP with Server‑Sent Events for remote communication.
MCP Servers act as lightweight bridges to external systems by receiving client requests, interacting with data sources or tools, and returning formatted responses to the client.
MCP Clients are responsible for constructing context requests, handling responses, and optionally supporting features like recursive sampling for advanced LLM interactions.
The protocol specification also defines utilities for configuration management, progress tracking, cancellation, error reporting, and logging to ensure robust and observable integrations.
MCP is modular and extensible, reducing the need for bespoke connectors and enabling real‑time context enrichment and cross‑platform interoperability.
Common use cases for MCP include AI‑powered IDE assistants, customer service chatbots, knowledge management systems, content creation tools, financial analysis platforms, and healthcare applications.
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