MCP servers defined: what they are and how they work

An MCP server is a program that uses the Model Context Protocol (MCP) to give AI applications a standard way to reach external tools, data, and prompts, instead of a custom integration for every source. A host application, such as an AI assistant, connects to it as a client to retrieve or act on data. MCP servers matter because they let AI tools reach governed, up-to-date data through a single consistent interface, rather than a fresh build for each tool.

Ask ten people what an MCP server actually does, and you’ll probably get ten slightly different answers – some too technical, some too vague. This article gives you a clear definition of an MCP server, then walks through how these servers work, the main types you'll come across, and the functions and advantages that explain why MCP has moved from a niche protocol spec into something showing up across AI and data tooling.

If you're new to MCP servers, you're probably trying to understand the mechanics before deciding whether – or how – to use one. That's what this piece covers: the client-server model behind MCP, the tools, resources and prompts a server can expose, and where a real MCP server fits into a stack that connects AI tools to actual business data.

We'll keep this practical rather than academic, using one working example throughout: an MCP server that exposes verified company and contact data to an AI agent.

What is an MCP server?

An MCP server is a program that exposes data, tools, or prompts to an AI application through the Model Context Protocol, so the AI doesn't need a custom integration built for that source. It's the piece of software that does the ‘connecting’. Think of it as a standard plug that any compatible AI tool can use, rather than a one-off cable built for a single device.

In practice, this means one MCP server can serve several different AI applications at once. A single server exposing verified company and contact data, for example, could be used by an AI assistant checking a company record, an agent building a list of leads, or a chatbot answering a rep's question about an account – all through the same standard connection, with no separate integration built for each one.

Instead of a bespoke build for every AI tool that wants to reach a given data source, one server can serve all of them.

How does an MCP server work?

At a basic level, an MCP server works on a client-server model. A host application – an AI assistant or agent – connects to the server through a client, and the server exposes its capabilities in a standard format the host can understand. This is true whether you're asking how an MCP server works or, more broadly, how MCP works as a protocol: the mechanism is the same one described below.

The client-server model

Three roles sit inside this model:

• The host is the AI application itself – the AI assistant, agent, or tool the person is actually using.
• The client is the part of the host that speaks MCP, sending requests to a server and handling its responses.
• The server is the program that exposes data, tools or prompts, and responds to what the client asks for.

Using the worked example: an AI assistant (the host) uses its built-in MCP client to connect to a company-data MCP server. When someone asks the assistant to check a contact's details, the client sends that request to the server, which looks up the record and returns it in a format the assistant can use directly. 

Tools, resources, and prompts

An MCP server can expose three kinds of capability:

• Tools – actions the server can perform, such as looking up a contact record or updating a field.
• Resources – data the server can return, such as a company profile or a list of matching accounts.
• Prompts – ready-made templates the server can offer, such as a standard way of asking for an account summary.

A single server rarely needs all three to be useful. A data-focused MCP server, for instance, might lean heavily on resources and a handful of tools, while a workflow-automation server might expose more tools than resources.

What are the main types of MCP servers?

MCP servers are commonly grouped by how they run and how they're accessed, rather than by what they do.

• Local servers run on a person's own machine, often used for things like reading local files or running scripts. They're straightforward to set up, but only available to whoever's running that machine.
• Remote servers are hosted elsewhere and accessed over a network, so several people or applications can reach the same server.
• Managed or hosted servers are offered by a vendor, who runs and maintains the server so the user doesn't have to. This is an increasingly common type as more vendors add MCP access to data or tools they already provide.

What can an MCP server do?

An MCP server's core job is to expose one or more capabilities – data retrieval, actions, or context – to any AI application that connects to it, through a consistent interface. In practice, that breaks down into a few recurring functions.

• Data lookup – returning records, files or other information on request – for example, retrieving a company's firmographic details.
• Running an action – performing a task on the AI's behalf, such as updating a record or triggering a workflow step.
• Supplying context – giving the AI extra background it wouldn't otherwise have, such as a summary of recent activity on an account.

Because these functions sit behind one standard interface, an AI application doesn't need to know the details of how the underlying data is stored or structured. It just needs to speak MCP.

What are the advantages of using an MCP server?

The main advantage is that one MCP server can serve many AI tools through the same standard interface. This means you don’t need to build and maintain a custom integration for each one.

That has a few practical knock-on effects. Integration overhead drops because a new AI tool can reach the same server without a fresh build. Data also stays usable as the AI tools around it change – a server built today keeps working with whatever AI assistant a team adopts next, rather than needing to be rebuilt each time.

There's also a governance advantage. A well-built MCP server can be a controlled, auditable way for AI tools to reach data – closer to a governed API than an unmanaged bulk export. For a company like Cognism, built on ISO 27001-certified, GDPR-first data with Do Not Call checks built in, that governance layer matters as much as the connection itself: an AI tool is only as trustworthy as the data and access controls behind the server it's talking to.

For most organisations starting out with MCP connectors, that governance question starts even earlier than data access. Who in IT approves a new MCP server for use at all? Many admin consoles can allow-list or block servers before anyone gets that far.

Getting started with MCP servers

Understanding how an MCP server works is the foundation for deciding whether – and how – to connect AI tools to your own data. The mechanics covered here apply regardless of the server you're evaluating, from a simple local file server to a managed, vendor-hosted one.

If you're weighing up MCP against a traditional API for a specific integration decision, that comparison deserves its own look. And if you're trying to work out what MCP means for your wider GTM stack rather than just the mechanics, that's worth a separate read too.

If you want to see a working example rather than just read about one, keep an eye out for Cognism's upcoming MCP connector.

Frequently asked questions