Guide
What is MCP and why should businesses care?
MCP is becoming a practical bridge between AI systems and real business tools. The business question is not whether agents sound impressive. It is whether they can safely find, understand, and use your company.
Updated June 18, 2026
The short version
The Model Context Protocol gives AI applications a standard way to connect to external systems, data, tools, and workflows. For a business, that means a product catalog, quote path, support process, policy library, or review workflow can become something an AI client can discover and call in a structured way.
A website is still necessary, but it is no longer the only interface. People read websites. Agents need structured data, tool schemas, authorization rules, pricing boundaries, support paths, and audit trails.
Why this matters commercially
Most companies have information scattered across landing pages, PDFs, forms, inboxes, portals, and internal systems. That is workable for a human buyer with patience. It is brittle for an agent trying to compare vendors, prepare a quote request, or route a support issue.
MCP does not replace the website or the API. It sits beside them as a controlled action layer. A good MCP surface tells an AI client what tools exist, what inputs are required, what the output means, and what safety class applies.
- Catalog tools let agents understand what you sell.
- Quote and support tools let agents start a workflow without pretending to complete it.
- Review tools let high-consequence work move through human approval.
- Audit events let the business understand what happened later.
The trap to avoid
The mistake is to publish tools before the operating rules are ready. A tool that can create, change, buy, refund, delete, or publish needs stronger controls than a tool that only reads public product data.
A practical first MCP is narrow. It should expose public-safe discovery, read-only catalog lookup, quote intake, support intake, and review packet preparation before any agent receives production write authority.
DID's recommended first move
Start with an Agent Readiness Audit. The audit identifies what agents can already understand, what structured data is missing, which actions are safe to expose first, and where human approval or SolaceSentry-style review gates are required.
From there, the business can decide whether it needs a public discovery cleanup, a private MCP server, commerce readiness, or a regulated workflow implementation.