The Model Context Protocol (MCP) provides a standardized method for applications to expose data and functionality to AI models. It enables models to interact with external tools, data sources, and systems in a safe, controlled, and structured way.
At its core, MCP uses a client-server architecture. The MCP Server exposes resources (like data) and tools (functions) through a defined interface. The MCP Client (typically an AI application or framework) connects to these servers to discover and utilize these capabilities, enriching the model's context and enabling actions.
This approach offers key benefits. It allows models to access real-time, proprietary, or large-scale data without being part of their core training. It also provides a secure framework for granting models controlled access to tools and systems, moving beyond simple text generation.
Ultimately, MCP functions as a universal connector, simplifying how AI models integrate with the broader ecosystem of software and data to perform more useful and grounded tasks.
{
"mcpServers": {
"howtocook-mcp": {
"command": "npx",
"args": [
"-y",
"howtocook-mcp"
]
}
}
}