How it works
An MCP server publishes tools, each with a name, a description and an input schema, plus resources the model can read. An MCP client, built into an AI application or agent framework, discovers the tools and calls them on the model's behalf. The protocol standardizes the plumbing, so one server can serve many AI clients without custom glue for each.
Example
A procurement agent connects to an MCP server that offers list_vendors, get_purchase_order and create_bill tools. The agent reads a purchase order, then calls create_bill with structured arguments rather than receiving raw API credentials.
In integrations
Unify Agents (MCP) is designed to expose integrations as MCP tools with scoped permissions, approval steps for sensitive writes and an audit log of every call. The aim is to give agents real tools, not keys. MCP itself does not provide governance: limits, approvals and audit are the job of the server and the platform behind it. MCP matters because agents are becoming a common way to use software. A product that exposes well-scoped tools can let agents do real work in customers' systems without handing a model unrestricted API access.