MCP Architecture Overview
MCP (Model Context Protocol) features a distributed architecture enabling AI applications to communicate seamlessly with multiple data sources and tools through standardized interfaces.
Core Components
The MCP architecture consists of three main components that work together:
-
The Host:
- Your AI application (e.g., IDE like Zed, VS Code, Cursor, Windsurf, Trae, etc.) this list seems to be growing really fast.
- Acts as the manager overseeing all connections
- Manages user interactions and permissions
-
MCP Clients:
- Dedicated communication handlers within the host
- Each client connects to one specific MCP server
- Manages the connection lifecycle and message routing
-
MCP Servers:
- Gateway to specific data sources or tools
- Examples include document stores, code repositories, databases
- Exposes capabilities using standardized MCP interfaces
Communication Protocol
MCP uses JSON-RPC 2.0 for structured communication between components:
Transport Types
-
Local Transport (stdio):
- Direct process communication
- Used for desktop apps and local development
-
Remote Transport (SSE/HTTP):
- Cloud-based deployment support
- Works through firewalls and across internet
- Enables web-based AI agent access
- Compatible with modern cloud platforms
Core Primitives
MCP defines three fundamental types of interactions:
-
Resources:
- Structured data (code, documents, query results)
- Application-controlled access
- Provides factual context to AI models
- Read-only information exchange
-
Prompts:
- Pre-defined instruction templates
- User-initiated usage
- Standardizes common operations
- Examples: code styles, documentation formats
-
Tools:
- Action-oriented capabilities
- AI model-initiated, user-authorized
- Performs concrete operations
- Examples: database queries, API calls
For detailed instructions on implementing MCP in your application, see the MCP Implementation Guide.
Related Articles
Docker Sandboxes and MCP
This section explores how Docker sandboxes can leverage the Model Context Protocol (MCP) to enhance their functionality and integration with other tools and services.
DALL-E Image Generation
A comprehensive guide to using DALL-E for AI image generation, including how to create, edit and manipulate images using OpenAI's DALL-E API, best practices for prompts, and practical examples for generating high-quality AI artwork.
Tfnsw realtime
Tfnsw realtime