What is an MCP server?

Imagine giving an assistant access to billing records. A custom connector needs to understand the billing platform and fit the assistant's integration interface. Adding a different assistant can mean another interface to maintain. Adding another business system adds another set of connections. Without a shared interface, the work can multiply with both the systems and the applications that use them.

MCP provides a common protocol on the AI application side. A server exposes selected capabilities, and compatible clients can use that interface. The official introduction to MCP describes this connection between AI applications and external tools and data.

That changes where custom work belongs. The vendor connection can sit behind the server, while each compatible application understands the shared interface. There is still work to configure access and verify compatibility. It is not a promise that every assistant can use every server without setup.

An agent may use such a connection while working through a task. If that term is also unfamiliar, start with what an AI agent is. The agent chooses steps; an MCP server makes particular capabilities available to it.

The AI application is the host. Its MCP client communicates with the server, which may run locally through standard input and output or remotely through Streamable HTTP. The MCP architecture guide defines those roles and transports.

1. Discover the available operations. The client can request a list of tools with names, descriptions, and input schemas. The assistant application can give those descriptions to the model so it knows which requests it can propose.
2. Request a tool call. Based on the task, the model can select a tool and supply arguments. The host application decides how to handle that request, including any required user confirmation. Its client sends the call; the model does not directly execute the vendor's code.
3. Check, execute, and return. The server validates inputs, enforces access controls, and carries out the allowed operation. It returns a result or an error that the application can use. These tool behaviors are described in the MCP tools specification.

Servers can also expose resources: addressable data such as files, database schemas, or application records. The client can read them, and the host controls their use as context. Resources and tools serve different purposes; the resources specification explains the distinction.

For a vendor-backed server, keep vendor credentials in the server-side connection layer, outside the model's prompt and tool arguments. The application and server must implement the appropriate authorization for their deployment; the protocol does not turn a model's request into permission.

HDS's vendor middleware work involved billing, telecom, and PSA platforms. Core business data was locked behind billing, telecom, and PSA platforms whose APIs paginate inconsistently, encode fields as opaque integer picklists, and return success codes on authentication failures.

An MCP server does not repair those behaviors. A tool called "find account" still needs a client that retrieves the complete result, translates vendor fields, and recognizes failure. Wrapping a fragile request in a standard interface leaves the request fragile.

The published build included typed async clients handling pagination, rate limits, and picklist resolution; MCP servers exposing the systems to AI tooling; and middleware that normalized the vendor payloads into a stable internal shape. Each layer had a distinct responsibility.

The practical design lesson is to keep that vendor knowledge reusable. A scheduled workflow and an assistant asking about an account should not carry different definitions of the same status field. Putting the interpretation in the client gives both a shared place to obtain it and a shared place to repair it when the vendor changes.

Operations data is queryable in one interface instead of three portals, and the same clients back both automation and assistant workflows. Read about the vendor platforms these were built over. This is why building an MCP server for a business system includes integration work, even when the protocol-facing tool list looks small. A clear interface is valuable because the underlying operation has been made dependable enough to expose.

An MCP server can return a real record and the model can still misunderstand it. A successful lookup does not establish that the answer used the right account, interpreted the fields correctly, or had enough context for the business decision. Design the result so a person can inspect the source and recognize uncertainty.

Access also needs an explicit policy. An available operation can be requested; the server must decide whether that caller may perform it on that record. A prompt asking the model to be careful cannot replace those checks. The MCP authorization specification covers restricted access for HTTP deployments, with different credential handling for local standard-input/output servers.

For a business deployment, distinguish looking up information from changing it. Decide which changes require a person to review the exact proposed action. Keep the exposed operations narrow enough to explain, and make failures visible.

MCP is useful when an AI application needs access to a capability and the server can expose that capability with clear boundaries. It does not decide whether AI is the right interface for the task. A scheduled transfer with known rules may still be better expressed as ordinary automation using the same underlying client.

Before choosing the interface, describe the intended result in business terms. Which record does somebody need? Which action should be possible? Who can authorize it? What should happen when the system cannot answer? Those questions identify the connection and its limits without assuming the protocol is the whole project.

The broader practice is integration and middleware. MCP is one way to make that work available to an assistant, alongside other interfaces that can use the same maintained connection.

Explore the integration practice for the vendor-facing work, or browse the published engagements to see how these connections fit into operational systems.