Model Context Protocol (MCP) explained: An FAQ

The Model Context Protocol (MCP) is a specification designed to standardize the way large language models (LLMs) access external data and systems, extending their capabilities beyond their training data. By acting as a universal connector between AI models and applications, MCP enables developers to expose data sources and tools in a structured manner that AI models can understand, allowing for controlled and predictable interactions. Unlike traditional APIs that require hard-coded calls, MCP uses metadata and structured descriptions to help models decide which tools to use, thus reducing the need for custom integrations for each AI platform. This approach facilitates AI models in performing actions based on real-time data and functions, enhancing their accuracy and utility. MCP servers can be hosted locally or remotely, enabling models to interact with real systems across various domains such as e-commerce, finance, and customer support. While the protocol is open source, created by Anthropic, it is supported by a community of developers and offers a secure way to integrate AI with existing systems. MCP's introduction is comparable to how standards like HTTP and REST revolutionized the web, by providing a consistent framework that reduces integration costs and unlocks new application possibilities.