What is MCP (Model Context Protocol)?

Introduction to MCP

MCP is a standard way to connect an AI application to the data and tools it needs to do useful work. A language model on its own only knows what it was trained on. To act on current, specific information, such as a company's files, a database, or a live service, it has to reach outside itself, and MCP defines how that connection is made.

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The problem it solves is integration sprawl. Before a shared standard, connecting an AI application to each external system meant building and maintaining a separate, custom integration for every pairing, which grew unmanageable as the number of models and tools rose. MCP replaces those one-off connections with a single protocol, so any application that speaks MCP can work with any tool that exposes an MCP interface. It is often compared to a universal port like USB-C: one standard connector in place of a drawer full of adapters.

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The standard was introduced in late 2024 and is now maintained as an open-source project under the Linux Foundation, with support across major AI providers. Because it is open, any vendor can implement it on either side of the connection.

How MCP works

MCP uses a client-server design with three roles.

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• Host: the AI application the person interacts with, such as a chat app, a coding tool, or an AI agent. It manages the model and decides when to reach an external tool.
• Client: a connector that lives inside the host and holds a one-to-one connection to a single server, handling the messages between them.
• Server: a program that exposes a particular system's data and capabilities, such as a file store, a database, or a business application, in the standard MCP format.

A host can run several clients at once, each connected to a different server, which is how one AI application reaches many systems through the same standard. The pieces communicate using JSON-RPC, a common message format, over one of two channels: a direct local connection for a server running on the same machine, or an HTTP connection for a remote server reached over the network.

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What a server exposes falls into three kinds of capability. There are tools the model can invoke to do something, resources it can read as context, and prepared prompts a user can call on. When a host connects, it discovers what a server offers, so the model can use those capabilities without being programmed for each one in advance. The MCP server page covers these in more detail.

Why MCP matters for AI agents

MCP is closely tied to the rise of agentic AI, because an agent is only as useful as the tools and data it can reach. An agent that can plan steps still needs a way to act on real systems, and MCP gives it a standard means to do so, along with current data to reason over instead of relying on its training alone. The same standard also lets an agent carry context from one tool to another, so each integration is not a separate island.

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Because a server can expose actions that change real systems, connecting one is a matter of trust. A server defines what a connected model is allowed to do, and a model can be misled by instructions hidden in the data it reads, so access is normally scoped to what is needed and sensitive actions are kept under human approval. Treating a connected server with the same care as any other integration with access to real data is part of using MCP safely.

How Atlar uses MCP

Atlar uses MCP to make your treasury data reachable from the AI tools you already work in. The connections to your banks and ERP are built and managed by Atlar, consolidating balances and transactions in real time, and MCP exposes that live data to a connected assistant. Through it, you can query your accounts, balances, transactions, and 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.

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Companies like Lovable and Trustly run their treasury on Atlar. To see how Atlar's AI works, see Atlar Intelligence, or book a demo with our team.