Every brief in this series says “agent” and “tool” as if you already know what they mean. This is the one that explains them.
A chatbot has a conversation. You ask, it answers, from what it already knows. An agent has a job list. It can stop mid-answer, go get something it does not know, and come back — then decide if it needs to go get something else before it is done.
Every one of those trips is a tool call. A model can write and reason, but it cannot check today's date, look up a record, or send an email by itself. A tool is what gives it hands — one specific, narrow action it can trigger.
A tool can be almost anything a business already uses. If a person could look something up or take an action there, an agent can be handed a tool that does the same thing — including searching the open web.
Steps 2 and 3 repeat as many times as the job needs — the agent decides, one trip at a time.
“What is my copay for tomorrow's visit?” The agent makes two tool calls to find out.
Without a shared standard, every agent needs custom wiring for every tool it might use — one connector per pair, rebuilt each time either side changes.
MCP — Model Context Protocol — fixes that. It is a shared job description any tool can implement. Once a tool speaks it, any agent that also speaks it can find that tool, see what it does, and use it. Think of it like a wall socket instead of a bare wire: once something has the right plug, it works in any socket built to the same standard.
MCP is not one company's product — it is an open specification. MongoDB, for one, now ships its database as an MCP tool, so any MCP-speaking agent can query it with no custom connector.
Every tool a model can call is also a door. A standard that makes doors easy to add makes it easy to add too many. An unreviewed tool is the same risk as an unapproved app — just faster, and running all day.
MCP is the plug. It is not the policy. Standardizing how an agent finds and calls tools makes adoption fast — which is exactly why deciding, deliberately, which tools it is allowed to use matters more, not less.
Full paper, 10 pages — iterate.ai/partners/netapp/papers. On deciding which model handles a job, see What Is a Harness?; on seeing every call after the fact, You Cannot Govern What You Cannot See.
A joint educational series on private AI and the AIPod Mini. NetApp® — the governed data-control layer. Iterate.ai — the private intelligence layer.
