MCP Explained: Connecting AI Agents to Your Business Tools

Connecting an AI agent to your ERP, your CRM or your document store used to mean custom development for every single connection. The Model Context Protocol changes that. Here is what it is, how it works, and what it means for the security of your systems.

What problem does MCP actually solve?

An AI agent, as we defined it, is only as useful as what it can reach. To act on an ERP, read a document store or write to a CRM, it needs a connection. Without a common standard, every connection is a custom integration: proprietary code, ongoing maintenance, and dependency on a single AI provider. With five business tools and two different AI models, you end up managing ten separate integrations.

This is the classic N×N problem: N agents multiplied by N tools, and as many integrations to build and maintain. The Model Context Protocol (MCP) reduces that to N+N. Each tool exposes an MCP server once. Each agent knows how to read MCP servers. The connection becomes standard, reusable, and independent of the provider.

How does the Model Context Protocol work in practice?

MCP defines a common language between an agent and a tool. The protocol distinguishes three elements: resources (data the agent can read), tools (functions the agent can call), and prompts (predefined templates that guide the agent).

In practice, an agent sends a structured request to the MCP server: "Read order number 4821" or "Create a customer record with this information." The server receives the request, translates it into an API call to the relevant software, and returns the response in a format the agent understands. The agent never needs to know the technical details of the target software.

Launched by Anthropic in November 2024 and quickly adopted as an open standard, MCP is now documented publicly at modelcontextprotocol.io and supported by a growing ecosystem of open source connectors. Adoption has been fast: within months, dozens of software vendors had published their own MCP servers.

What exactly is an MCP server?

An MCP server is the software component that links the agent to a business tool. Think of it as a bilingual interpreter: on one side, it speaks the MCP language the agent understands; on the other, it speaks the native language of the software (its API, its database, its files).

An MCP server declares its capabilities at startup: "I can read orders, create invoices, list products." The agent queries this list and immediately knows what it can and cannot do with that tool. This explicit declaration is also a control mechanism: what is not declared is not accessible.

MCP servers already exist for many everyday tools: Slack, GitHub, Google Drive, Notion, PostgreSQL. For an ERP or a proprietary business system, custom development is still needed, but only once. The second integration costs a fraction of the first.

How do you connect an agent to an ERP or a CRM using MCP?

Integrating an agent into your IT systems follows a simple logic with MCP. You deploy an MCP server in front of the target software (on premise or in the cloud), declare the functions the agent is allowed to use, and connect the agent.

In practice, for an ERP such as SAP, Sage or Odoo, the MCP server exposes the relevant business operations: check stock, read an order, create a credit note. The agent only has access to those specific operations, not the entire system. That is a key difference from a direct API integration, where the access scope is often broader than necessary.

For more on building agents and choosing an architecture, see how to build and deploy an AI agent, which walks through the process end to end. If you are starting without in-house technical resources, no-code AI agent tools compared covers the no-code platforms that rely on MCP to connect to your tools.

Microsoft has integrated MCP into its enterprise AI tools. Microsoft's overview of MCP on Microsoft Learn describes how the protocol fits into the Azure AI Foundry ecosystem, which gives a sense of how far the standard has already spread in industry.

How does MCP handle access security?

Security is the point most often misunderstood. MCP does not handle authentication itself: each MCP server delegates that to the software's existing authorisation mechanisms (OAuth, API key, token). What MCP adds is granularity.

An MCP server only grants access to explicitly declared functions. If you declare "read orders" and "create quotes," the agent cannot delete a customer record, even if it asks. This server-level control is complemented by deployment-level control: you decide which identities can call which MCP server.

A few principles to apply in practice. First, least privilege: declare only what the agent needs for its task, nothing more. Second, traceability: every MCP request can be logged, which allows a full audit of what the agent did and when. Third, environment separation: a staging MCP server and a production MCP server, with distinct permissions.

These guardrails matter particularly when the agent works with financial data or customer information, two situations that come up often in AI agent deployments for SMEs and mid-sized companies. An upfront AI assessment helps evaluate the risks and size the controls before any deployment.

MCP or a custom integration: how do you choose?

MCP is not the answer to every need. It shines in three situations: when several agents need to access the same tools, when you want to be able to switch AI models without rebuilding everything, and when you want to capitalise on the open source connectors already available.

A custom integration still makes sense when requirements are very specific (high-frequency real-time flows, complex data transformation) or when the target software does not expose a stable API to build an MCP server on. Some legacy systems fall into that category.

In most SME or mid-market projects, MCP reduces the cost of the first integration, and especially of the next one. An agent connected to the ERP can be extended to the CRM within hours, not weeks. That cumulative effect is why it is worth adopting the standard from the first project, rather than considering it retrospectively after several proprietary integrations.