Model Context Protocol (MCP)

An open standard, the Model Context Protocol (MCP), that lets any AI application connect to tools and data sources through a shared interface, so you build the integration once and every compatible AI app can use it.

Before the Model Context Protocol (MCP), every AI tool needed its own custom code to connect to your files, databases, calendars, or APIs. MCP standardizes that connection: you set up an MCP server that exposes your tools and data, and any MCP-compatible AI application (Claude, ChatGPT, Cursor, coding agents, custom apps) can discover and use them through the same protocol. Think of it like USB for AI integrations: one plug, many devices.

Builder example

If you build a Model Context Protocol (MCP) server for your company's data, every AI tool your team uses can access it without separate integration work. This is especially powerful when your team uses multiple AI applications; you write the connector once and get access everywhere. The tradeoff is that anything your MCP server exposes becomes available to any connected AI, so scoping access carefully matters.

You want Claude, Cursor, and another agent to query the same project notes without rebuilding the integration three times.

Expose the notes through a Model Context Protocol (MCP) server, then keep permissions narrow and version the server contract.

Common confusion: The Model Context Protocol (MCP) handles the connection, not the security. The tool descriptions and data that flow through it become part of what the AI trusts and acts on, so a poorly scoped MCP server can give an agent far more access than you intended.