MCP, the Model Context Protocol, is an open standard that gives AI agents one common way to connect to tools and data. Anthropic released it in late 2024, and through 2025 it became the default way to plug agents into the systems where work actually happens. If your team plans to let an agent touch email, orders, or a database, MCP is probably the plumbing it arrives through.
The problem it solves
Before MCP, every agent-to-tool connection was a custom integration. Five agents and six internal tools meant thirty separate integrations, each with its own auth, its own bugs, and its own maintainer who left last spring. The usual analogy is USB-C: one port, any device. It’s accurate.
With MCP, a tool gets wrapped once as a server, and any MCP-capable agent can use it. Claude Desktop, Claude Code, and a growing list of other clients all speak it, and so can an agent your own team builds.
The pieces, in plain terms
- A host is the agent application: the thing the person talks to.
- A server exposes a set of capabilities: your CRM, your order system, your file store.
- A tool is a typed action a server offers, with a defined input and output:
get_order(order_id),issue_refund(order_id, amount).
A concrete flow: a customer asks about a refund. The agent calls get_order on your commerce server, reads the details, and calls issue_refund. The server defines what’s possible. The agent decides what to call. Hold that thought, because it’s where this gets interesting.
Why business teams should care
You integrate once, not per agent. You can swap models or add agents without rebuilding the plumbing, which keeps you out of vendor lock-in on the fastest-moving layer of the stack. And a catalog of ready-made servers already exists for common systems, so "connect the agent to X" increasingly means configuration, not an engineering project.
What MCP doesn’t solve
MCP standardizes connection, not judgment. A tool being reachable says nothing about whether this agent, on this task, for this customer, should call it. Your refund cap, your discount policy, who approves exceptions, when to stop and ask: none of that lives in the protocol, and it was never supposed to.
That gap is what we built Brian for. Brian is an MCP server whose tools serve the company’s rulebook: find_skill returns the governed procedure for a task with its hard rules and stop conditions attached, and log_execution writes the audit trail. One connection, and every agent you run carries the same judgment. What those skills look like inside is covered in SOPs for AI agents.