Model Context Protocol (MCP)
What is Model Context Protocol?
Model Context Protocol, or MCP, is an open protocol that standardizes how AI applications connect to external context and capabilities. Instead of building a different custom integration for every application and service, developers can implement a common client-server interface.
MCP focuses on context exchange and capability access. It does not define how an AI application must reason, choose tools, or manage the information it receives.
How MCP is structured
MCP uses a host-client-server architecture:
- The host is the AI application coordinating integrations.
- An MCP client connects the host to one MCP server.
- An MCP server exposes context or capabilities.
The protocol uses JSON-RPC messages. Servers can expose resources, prompt templates, and tools. Clients can discover available capabilities and request them through standardized protocol operations.
For example, an IDE can act as an MCP host and connect to servers that expose source repositories, issue trackers, or development tools.
MCP versus an API
An API is a general interface between software systems. MCP is a protocol designed specifically for connecting AI applications with context and capabilities. An MCP server may wrap one or more APIs, local files, databases, or computations.
MCP does not eliminate APIs. It provides a consistent AI-facing layer over different implementations.
MCP versus tool calling
Tool calling is the model or agent requesting a defined capability. MCP can standardize how tools are discovered and invoked between a client and server. The host still decides which capabilities to present to the model and whether a requested call is allowed.
The protocol is not an authorization decision by itself. Implementations must enforce credentials, permissions, user consent, and safe handling of results.
MCP versus A2A
MCP connects an AI application to data, prompts, and tools. Agent2Agent Protocol focuses on communication and task collaboration between independent agent systems. An agent may use MCP for its capabilities and A2A to work with another agent.
They solve different integration boundaries and can be complementary.
Why it matters
MCP can make integrations reusable across compatible hosts and servers. It separates capability providers from the internal design of an AI application and reduces one-off connection logic.
That interoperability also expands the attack surface. Hosts should expose tools clearly, validate results, apply least-privilege access, and require confirmation for sensitive actions.