Model Context Protocol, known as MCP, is an open standard that lets AI applications discover data and tools through a consistent interface. Instead of creating a unique connection for every model and system, an MCP server presents resources, prompts, and tools in a format the client understands.
API vs MCP
An API defines how software calls an operation. MCP adds a discovery and context layer designed for models: which tools exist, what they do, and what input they require. Standard APIs can still sit underneath. MCP packages them into a consistent interface for agents rather than replacing them.
A business example
A sales agent may need to read a customer record, check an order, retrieve a document, and schedule a meeting. Instead of rebuilding four integrations inside every AI application, each system can expose narrowly scoped MCP tools. This makes changing the host or adding another agent easier.
When MCP pays off
MCP is useful when several agents need the same tools, integrations change frequently, or the company wants to avoid dependence on one model vendor. For one stable workflow using a single API, a direct integration can remain simpler and cheaper.
Permissions before convenience
An MCP server may expose real data and actions. Apply least privilege, separate reading from writing, require confirmation for sensitive actions, preserve audit logs, and never treat a tool description as a security control.
How to start
Select one process, define three to five narrow tools, use a test environment without real customer data, and evaluate malicious input and system failures. Connect production data only after permissions and success metrics are understood.