MCP Explained

AI apps can now connect to custom tools and data without custom integrations for each one.

The Model Context Protocol (MCP) is an open standard designed to connect AI applications with the tools and data they require. It functions by having a server describe its capabilities to an AI app. When the AI model determines it needs to use a tool or access data, the app then calls the server, which executes the request and returns the result. This protocol supports various AI applications like Claude, Cursor, and ChatGPT, as well as AI agents. Servers are small programs that expose tools, data, or prompts. Importantly, the AI model itself does not directly communicate with the server; instead, the application acts as an intermediary. MCP utilizes JSON-RPC over stdin/stdout or a single HTTP endpoint for communication, but it defines the specific structure of the data within the JSON-RPC envelope, including methods, capability negotiation, transports, and authentication. The specification was developed by Runlayer.

MCP aims to standardize how AI models access external resources, moving away from bespoke integrations for each tool or data source. By defining a common protocol for capability negotiation and tool invocation, it lowers the barrier for AI developers to leverage a wider array of functionalities. This could lead to more sophisticated and capable AI applications that are easier to build and maintain.

The impact is a more modular and interoperable AI ecosystem. Developers can create reusable tool servers that any MCP-compliant AI app can discover and utilize. This shifts the focus from building monolithic AI applications to orchestrating specialized components, potentially accelerating innovation and democratizing access to advanced AI capabilities.