TLDR
- AI that only produces insight does not change the business; MCP gives assistants a defined way to act on real systems.
- One MCP server can serve several AI clients, including ChatGPT, Claude and Grok.
- Before building, decide who the agent is, what it can do, how failure looks, and how actions are logged.
What this session covers
Most AI initiatives stop at insight. A model can summarize, draft and recommend, but nothing changes in the business until something acts on the answer. This MCP 101 webinar explains how the Model Context Protocol connects models, data and systems into an operational layer, so assistants like ChatGPT, Claude and Grok can work with real retail and ecommerce systems instead of only talking about them.
MCP in plain terms
MCP is an open protocol that lets an AI client discover and call tools exposed by a server. The server decides what is available, what each tool accepts, and who is allowed to use it. The client can be any assistant that speaks the protocol, which is why one server can serve several different AI products. The Model Context Protocol specification defines how clients and servers talk to each other.
What to ask before you build one
Who is the agent? Give it its own identity and credentials that you can rotate, not a shared admin login.
What can it do? Expose a small, clear toolkit with schemas, not a wrapper around every internal endpoint.
What happens when it fails? Return honest answers when something is missing or unsupported.
How do you know it worked? Log every action the way you would for any production API.
These are the same questions we had to answer when we built MCP into the portal we use to run our own agency, which we describe in We Run Our Agency on MCP. For retail teams, The Agentic Shift covers why an MCP server is becoming part of the ecommerce stack.
If you want help scoping one for your catalog and back-office systems, see our MCP server development and conversational search solutions.