A universal standard for connecting AI models to external tools and data sources
The Model Context Protocol (MCP) is an open standard that provides a universal way to connect AI models and agentic applications to various data sources and tools. It enables AI applications to supply context (documents, database records, API data, web search results, etc.) to AI models.
MCP follows a client-server model with clear separation of roles. An MCP Host (the AI application or agent) connects via an MCP Client library to one or more MCP Servers. Each server exposes a specific set of capabilities (such as reading files, querying a database, or calling an API) through a standardized protocol.
This is why people often refer to MCP as the "USB-C port for AI applications" - it creates a standardized interface for AI models to interact with external tools and data sources.
MCP solves several critical challenges in AI application development:
By design, the MCP architecture introduces security boundaries: the host and servers communicate only via the MCP protocol, which means security policies can be enforced at the protocol layer. For example, an MCP server can restrict which files or database entries it will return, regardless of what the AI model requests.
Connect AI models to databases, APIs, file systems, and other services
Provide additional information and data to improve AI responses
Enable AI systems to perform complex, multi-step tasks using external tools
Create and share reusable tools across different AI applications
While MCP is powerful, it also requires careful security implementation:
The clear delineation of components in MCP makes it easier to apply the Zero Trust principle (treating each component and request as potentially untrusted until verified).