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MCP Overview

Introduction

The Model Context Protocol (MCP) is a standardized protocol for communication between AI models and external tools, resources, and services. This guide provides comprehensive best practices for developing, deploying, and maintaining MCP servers.

What is MCP?

MCP enables AI assistants to interact with external systems through a well-defined protocol, allowing them to:

  • Access and manipulate data from various sources
  • Execute tools and functions
  • Provide contextual information to enhance AI responses
  • Maintain secure and controlled interactions with external services

Why MCP?

Key Benefits

  • Standardization: Consistent interface across all integrations
  • Security: Built-in authentication and authorization mechanisms
  • Scalability: Designed for distributed architectures
  • Flexibility: Support for multiple transport protocols
  • Observability: Built-in monitoring and logging capabilities

Core Components

  1. MCP Servers: Expose tools, resources, and prompts
  2. MCP Clients: Connect to servers and utilize their capabilities
  3. MCP Gateway: Central hub for managing multiple MCP servers
  4. Transport Layer: Communication protocols (STDIO, HTTP, WebSocket)

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