What is an MCP server?

Introduction to MCP servers

An MCP server sits between an AI application and some system it needs to reach, such as an accounting platform, a code repository, or a company knowledge base, and presents that system's capabilities in the shape the MCP defines. The AI application does not need to know how the underlying system works; it only needs to speak the protocol.

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A useful way to picture it is as an adapter. The system on one side has its own API, data format, and rules; the AI application on the other side speaks only MCP. The server translates between the two, so the same AI tool can connect to many different systems, each through its own server, without bespoke code for each.

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Servers are usually small, focused programs. One server typically covers one system or domain, such as a file store or a single application, which keeps each one simple and means a host can combine several to reach everything it needs. They can be written in any common programming language, since what matters is that they communicate in the protocol's format.

What an MCP server exposes

A server offers its capabilities as a defined set of building blocks, which an AI application discovers when it connects rather than having them hard-coded in advance. There are three kinds.

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• Tools: the actions a server makes available, each with a defined name and a description of what it does and what inputs it takes, so a model can decide when to call it. A tool is what lets the AI change something or fetch something on demand, not just read.
• Resources: data the AI can read, such as files, records, or the output of a report, which the model draws on as context for its answer.
• Prompts: prepared instructions or templates the server makes available for a user or model to call on for a common task.

When an AI application connects, it asks the server what it offers and receives the list in reply, so it can start using a server's tools and data straight away.

Local and remote servers

Where a server runs shapes how it connects. A local server runs on the same machine as the AI application and communicates directly with it, which suits reaching things on that machine such as local files or a script. A remote server runs elsewhere and is reached over the network using a web connection, which is how a hosted service exposes itself to many users. In both cases the server speaks the same protocol; only the channel differs.

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Many servers are already built and shared, including ones for common systems such as file storage, developer tools, and business applications, so connecting a widely used system often means using an existing server rather than writing one. Where a system has no server yet, building one is how it becomes reachable by any MCP-capable AI application.

MCP servers and AI agents

MCP servers give an AI agent its reach. An agent can plan a task, but to act on it, to read a record or make a change in a real system, it needs a way in, and a server is that way in. Because one host can connect to several servers at once, an agent can draw on many systems through a single standard, which is part of what makes broader, multi-step work possible.

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That reach is also why a server deserves care. Whoever runs a server decides which actions it offers and how much of a system it can touch, so a well-designed server exposes only what a task calls for and leaves anything consequential behind a human approval step. There is also the reverse risk: the data a server hands back can carry instructions that try to steer the model, so a connected server is worth the same scrutiny as any other component wired into real systems.

How Atlar uses an MCP server

Atlar exposes your treasury data through an MCP connection, so an AI tool can reach it the same way a user inside Atlar would. The connections to your banks and ERP are built and managed by Atlar, consolidating balances and transactions in real time, and that live data is what the connection makes available. Through it, you can query your accounts, balances, transactions, or forecasts from inside Claude and get answers pulled live from Atlar.

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Access follows your existing user permissions and role-based controls, so Atlar's AI only surfaces data a user already has access to, and every query is logged in Atlar's audit trail.

AI-first teams like Lovable, AMI Labs, and Parloa run their treasury on Atlar. To see how Atlar's AI works, see Atlar Intelligence, or book a demo with our team.