🔗 Model Context Protocol (MCP) Overview
The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs). Think of MCP like a USB-C port for AI: it offers a universal way to connect AI models to various data sources and tools.
🚀 Why Use MCP?
- Plug-and-Play Integrations: Easily connect your LLM to a growing list of pre-built integrations.
- Provider Flexibility: Switch between LLM vendors without changing your integration approach.
- Security Best Practices: Keep your data secure within your own infrastructure.
🏗️ MCP Architecture
MCP uses a client-server architecture, allowing host applications to connect to multiple servers and data sources:
- MCP Hosts: Apps like Claude Desktop, IDEs (VS Code), or AI tools accessing data via MCP.
- MCP Clients: Maintain 1:1 connections with MCP servers.
- MCP Servers: Lightweight programs exposing capabilities through MCP.
- Local Data Sources: Files, databases, and services on your computer.
- Remote Services: External APIs and internet services.
This setup lets LLMs and AI tools securely access both local and remote data, enabling powerful, context-aware AI applications.
🚦 Getting Started with MCPs
To help code-to-cloud teams quickly leverage the Model Context Protocol, we’ve curated a list of recommended MCP servers that can boost your development productivity. These servers provide ready-to-use integrations with common data sources, tools, and services.
Explore the submodule below for setup instructions, usage examples, and best practices:
- Getting Started with MCP Servers
A curated guide to installing, configuring, and using popular MCP servers for rapid integration with your LLM workflows.
Learn more: Model Context Protocol Introduction