MCP (Model Context Protocol) is an open standard that connects an AI to your business tools — CRM, ERP, databases, email, ticketing systems — in a uniform and controlled way. Initiated by Anthropic at the end of 2024 and widely adopted across the ecosystem since, it plays for AI the role USB played for peripherals: a universal connector replacing integrations built case by case.
The problem MCP solves
Without a standard, every connection between an AI assistant and a business tool is a specific development: one connector for the CRM, another for the ERP, a third for the database. With ten tools and two assistants, you maintain twenty integrations. MCP reverses the logic: each tool exposes its capabilities once through an MCP server, and any compatible assistant can connect to it. You move from a multiplicative problem to an additive one. It is this maintenance cost, even more than the initial cost, that has so far blocked connected AI projects in SMBs.
A standard adopted at record speed
A rare occurrence in the AI ecosystem, MCP was adopted within months by the main model providers and developer tool vendors, who integrated it into their products from 2025 onwards. Thousands of MCP servers are available today, covering most of the software on the market. For an SMB, this massive adoption is the best guarantee of durability: investing in an MCP connector is not a bet on proprietary technology. The protocol also evolves in the open, with a public specification and governance involving several players in the ecosystem.
What it is used for in practice
- CRM: asking the assistant for a customer history, creating an opportunity or drafting a meeting summary injected directly into the record.
- ERP and invoicing: querying stock levels, order status or unpaid invoices in natural language.
- Databases: getting a figure or an extract without writing SQL, with controlled read-only access.
- Support and tickets: summarising open tickets, qualifying a request, drafting a reply based on the real context of the case.
The common thread of these uses: the assistant no longer answers in theory, it acts on real, up-to-date data. That is what turns a conversational gadget into a daily productivity tool.
A simple architecture: client and server
MCP relies on three building blocks. The host is the AI application your teams use (desktop assistant, agent, internal application). The MCP server is a small program placed in front of each business tool, describing the available actions: search for a customer, read an order, create a ticket. Between the two, the MCP client handles the conversation under a common protocol. In practice, the assistant discovers the capabilities of a server on its own and knows how to use them, with no integration code needed on the assistant side. An MCP server can run on your own infrastructure, close to your data. For your technical teams, writing an MCP server amounts to cleanly describing a few business functions: official development kits exist in the main languages, and a first working server often fits in a few hundred lines of code.
The benefits over ad hoc integrations
The first gain is economic: a connector written once serves every current and future assistant, and the ecosystem already offers ready-made servers for most major tools on the market. The second is strategic: you are no longer tied to one AI vendor, since switching models does not invalidate your connectors. The third is operational: the exposed capabilities are explicit and documented, which makes auditing and governance easier.
Security and permissions: the point not to neglect
Connecting an AI to your systems demands a strict framework. Good practices are well known: give each MCP server minimal rights (read-only by default), use dedicated service accounts rather than the credentials of an employee, require human validation for any sensitive write action, log every call, and only install servers whose provenance is verified. A misconfigured MCP server is an entry point: treat it with the same rigour as an API exposed on the Internet. Also think about the data the model sees: if the assistant relies on a cloud service, the information read through MCP is transmitted to it, which must be covered by your contracts or by appropriate hosting.
Where to start as an SMB
- Identify a use case with immediate value, for instance read access to the CRM or to tickets to prepare meetings or support replies.
- Check whether an official or proven MCP server exists for the tool in question; if not, a custom server can be developed quickly on a restricted scope.
- Deploy in read-only mode with a small group of users, with logging enabled.
- Measure actual usage and gradually extend towards write actions validated by a human.
The approach is incremental by nature: each new MCP server adds capabilities without breaking anything that already exists.
MCP dramatically lowers the cost of connecting AI to your information system, but the value comes from choosing the right use cases and implementing them securely. It is exactly the kind of project where a few days of technical guidance save months of trial and error — and turn a generic assistant into a tool that truly knows your company.