mcp-tool

# 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:

1. **Messages API** - Developers can use the MCP connector to link to MCP servers programmatically
2. **Claude Code** - Supports adding custom MCP servers or using Claude Code as a server itself
3. **Claude.ai** - Teams can enable MCP connectors through the platform
4. **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:
```json
{
  "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

- Off