Every brief in this series says “agent” and “tool” as if you already know what they mean. This brief 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 to prepare its answer. It can stop mid-answer, go get something it does not know, and come back, and 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 the model reach: one specific, narrow action it can trigger.
A tool can connect with 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 each time the trip is run.
“What is my copay for tomorrow's visit?” The agent makes two tool calls to find out.
Without a shared standard, every agent would need 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 to the MCP, any agent that also speaks with that MCP can find that tool, see what it does, and use it. Think of it like an electric 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 just the standardized plug. It is not the policy that governs or allows access for a tool. Standardizing how an agent finds and calls tools makes adoption fast — which is exactly why deliberate control of 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.
