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The Model Context Protocol, or MCP, is an open standard that lets an AI agent connect to external tools and data through one common interface, instead of custom code for every system. In one line: what is MCP? It is the plug, not the AI itself — an 'MCP server' is what advertises a set of tools an agent is allowed to call, and the agent discovers and uses them through that one connection.
The Model Context Protocol (MCP) is an open standard that connects AI agents to external tools and data through one common interface.
The useful part is what MCP lets an agent do, not the protocol itself. Before MCP, connecting a Generative AI (GenAI) assistant — a chatbot or an autonomous agent, such as ChatGPT, Claude or Gemini — to a piece of business software meant writing bespoke integration code for that one system. MCP standardises that connection.
A system exposes an 'MCP server': a defined list of tools and data the agent may read or call. The agent connects once, asks the server what it can do, and uses whichever tools fit the task. What is the Model Context Protocol, in short? A common socket between AI agents and the systems they need to reach.

MCP itself is plumbing. The reason it matters is what it unlocks: a Generative AI (GenAI) assistant that can look at real data — orders, transaction statuses, reports — instead of guessing from a prompt alone.
| Without MCP | With MCP | |
|---|---|---|
| Connecting a new system | Every team wanting an AI agent to read their systems needed a custom integration per tool. | Any MCP-capable assistant can connect to any MCP server and discover what it offers. |
| What that takes | Bespoke integration code, built and maintained per tool, one at a time. | No custom code per system — the same way a browser can open any website without custom code per site. |
An MCP server is not the AI. It is the side that says what is available: a list of tools (actions the agent can call) and resources (data the agent can read), each with a description the agent uses to decide when to use it.
The agent — the chatbots and autonomous agents built on models such as ChatGPT, Claude or Gemini — is the side that decides, based on the user's request, which of those tools to call and how to use the result. The server sets the boundary; it only exposes what its owner chose to expose.
Payneteasy's own MCP server is called MCP Agent Access, and it is deliberately narrow: scoped to the connecting account's assigned merchant(s) and read-only.
See MCP Agent Access and the MCP integration guide for the full tool list.
MCP and agentic payments solve different problems and are easy to mix up.
An agent can use MCP purely to read and report, with no payment authority at all, which is exactly how Payneteasy's own MCP Agent Access is scoped today. See What Are Agentic Payments? for the initiating side of that distinction.
MCP is an open standard that lets an AI agent connect to external tools and data through one common interface, instead of a custom integration for every system it needs to reach.
An MCP server is the side of the connection that advertises which tools an agent may call and which data it may read. It sets the boundary on what a connected agent can actually do.
No. Payneteasy's MCP Agent Access is merchant-scoped and read-only. A connected agent can view transactions, statuses and reporting; it cannot authorise, capture, refund or move money, and it never touches raw cardholder data.
Any MCP-capable Generative AI (GenAI) assistant or agent — chatbots and autonomous agents built on models such as ChatGPT, Claude or Gemini — can connect to an MCP server, provided it supports the protocol.
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