majiayu000 Model Context Protocol (MCP)
Core Concept
MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
Purpose & Use Cases
MCP enables developers to connect AI models to custom tools and data sources in a standardized, interoperable manner—addressing a key need in extending Claude's capabilities beyond its base training.
MCP in Anthropic Products
The protocol integrates across multiple Anthropic offerings:
- Messages API - Developers can use the MCP connector to link to MCP servers programmatically
- Claude Code - Supports adding custom MCP servers or using Claude Code as a server itself
- Claude.ai - Teams can enable MCP connectors through the platform
- Claude Desktop - Allows local MCP server configuration
Build Your Own MCP Products
For protocol details, server/client development guidance, and community-created implementations, refer to the official MCP Documentation at modelcontextprotocol.io.
MCP in Claude Code
MCP Tool Integration
MCP tools follow the pattern: mcp__<server>__<tool>
Using MCP in Hooks
Configure MCP tools with regex matchers in hooks:
{
"matcher": "mcp__memory__.*",
"hooks": [{"type": "command", "command": "validate.py"}]
}
MCP Slash Commands
MCP servers expose prompts as commands with the pattern:
/mcp__<server-name>__<prompt-name> [arguments]
These are dynamically discovered from connected MCP servers and automatically available when the server is active.
Managing MCP Servers
Use the /mcp built-in command to manage MCP server connections.
Plugin Integration
Plugins can include MCP servers via .mcp.json configuration for external tool integration.
Resources
- Official MCP Documentation: modelcontextprotocol.io
- Anthropic's MCP implementation guides
- Community-created MCP servers and implementations