What Is an MCP Server (Model Context Protocol)?

MCP follows a client-server model. The AI application (the host) runs an MCP client; the system being connected runs an MCP server. When they connect, the server advertises what it offers, and the model can then invoke those capabilities mid-conversation. A server can expose three kinds of things: ²

• Tools: actions the model can take, like “search feedback,” “query a metric,” or “file a ticket”
• Resources: data the model can read, such as documents or records
• Prompts: reusable templates the server suggests for common tasks

The important design property is that the protocol is standard while the servers are specific. Your CRM, your data warehouse, and your feedback platform each run their own server with their own tools, but the agent speaks one protocol to all of them. Authentication and permissions sit with the server, which means the vendor controls what an agent can see and do rather than handing over raw database access.

Adoption spread quickly beyond Anthropic. OpenAI adopted MCP across its products in 2025, ¹ and vendors across the software landscape now ship servers, including customer-experience platforms such as Zendesk. (Disclosure: unitQ builds in this space.) In the feedback category specifically, unitQ ships an MCP server through agentQ, and Enterpret ships one as well.

Before MCP, giving an AI assistant access to company data meant one-off integrations, brittle plugins, or copy-pasting context into a chat window. A standard protocol changes the economics: build the server once, and it works with every current and future MCP-compatible agent.

For customer feedback in particular, the payoff is large because the questions are conversational by nature. “What are users complaining about since Tuesday’s release?” is exactly the kind of question a PM wants to ask in plain language and exactly the kind an agent can answer well, if it can query classified feedback rather than guessing from training data. An MCP server turns a feedback platform from a dashboard you visit into a capability your agents carry everywhere, including inside tools like Claude and ChatGPT that teams already use daily.

There is a governance angle too. Because the server mediates every call, teams can audit what agents accessed, scope permissions per user, and keep sensitive fields out of reach. That mediation layer is what makes agent access to customer data reviewable rather than a black box.