The Model Context Protocol (MCP) is an open standard that defines how an artificial intelligence application connects to external data sources and tools: a management system, a document library, an email service, a server. It is often likened to a USB-C port for AI: one standard socket instead of a different cable for every device. Published by Anthropic in late 2024 and documented at modelcontextprotocol.io, it has since been taken up across much of the ecosystem. For businesses, that is good news: connecting AI to your tools becomes simpler, more reusable and easier to secure.
The problem MCP solves
However capable it is, a language model only knows what it is given. For it to look up a customer file, create a quote or read a dashboard, it has to be connected to the software concerned.
Before MCP, every integration was built case by case: one connector for a given assistant and a given CRM, another for the same CRM and a different assistant, a third for the email system. This “N assistants × M applications” model multiplies development work, potential security holes and maintenance.
MCP replaces that sprawl with a common interface. Software exposed once through an MCP server becomes usable by any compatible application (the MCP client) — a desktop assistant, an AI agent or a bespoke application.
How does MCP work?
A client–server architecture
- The host is the AI application the person uses: an assistant, a code editor, a business application with AI built in.
- The MCP client, inside the host, manages the connection to each server.
- The MCP server exposes the capabilities of a system (an application, a database, an online service) in a standard format.
Messages are exchanged in JSON-RPC. A server can run locally on the workstation (talking over standard input and output) or remotely (over HTTP), with suitable authentication.
Three kinds of capability
An MCP server can expose:
| Capability | Role | Example |
|---|---|---|
| Tools | Actions the model can trigger | Create a quote, look up an invoice, send a message |
| Resources | Data the application can read | A document, a customer record, a configuration file |
| Prompts | Reusable instruction templates | “Summarise a file using our house template” |
Each tool is described precisely: its name, what it does, the parameters it expects. That description is what lets the model pick the right tool and call it correctly.
What MCP changes for a business
Reusable integrations. An MCP server built for your business software serves every AI use you have: the team assistant, an automated processing agent, a future tool. The investment is not lost the next time you change tools.
A growing catalogue. Many software vendors now ship their own MCP server, and servers exist for common building blocks (files, databases, developer tools, online services). Some integrations are therefore available without any development.
More capable agents. An AI agent is only useful if it can act within your tools. MCP gives it a standard way to do so — and therefore to carry out tasks that span several applications.
A single control point. Because every access goes through identified servers, you can control precisely what the AI may read and do, and log all of it.
Security: the essential precautions
Connecting AI to real systems demands genuine security discipline. MCP provides a framework, but security depends on how it is used.
- Least privilege. An MCP server exposes only the tools needed, with a service account holding limited rights. A read-only tool must not be able to write.
- Authentication. Remote servers must require strong authentication; the specification relies on OAuth for authorisation.
- Human approval. Binding actions (sending, paying, deleting, changing data) should be confirmed by a person, at least at first.
- Distrust of content. A document or web page may hide instructions meant to hijack the model (prompt injection). Limited rights and human approval reduce the impact.
- Trusted servers. Only install servers whose publisher and code you know, as with any software that touches your data.
- Logging. Every tool call must be traceable: who, when, which tool, which parameters.
MCP, APIs, browser automation: which route to take?
MCP does not replace APIs; it usually builds on them. An MCP server translates an API’s capabilities into a format AI applications understand.
- The software offers an official MCP server: the simplest route, provided you check its permissions and security.
- The software has an API but no MCP server: build an MCP server exposing only the useful functions, with the right guardrails.
- The service has no API at all (a government portal, an old extranet): the AI can drive a browser, confined to authorised sites and logged.
- No interface exists: processing by code (reading export files, for instance) takes over.
That is exactly the logic of our software AIKeep, which has these four access routes — APIs and MCP servers, browser automation, sandboxed code and system administration — to reach a company’s tools within a scope declared in advance.
A concrete example
Imagine a team that answers customer requests. Without MCP, someone reads the email, opens the CRM to find the customer, checks the order in the management system, then drafts a reply. With an assistant connected through three MCP servers — email, CRM and order management — the same request becomes a single instruction: “Draft a reply to this customer with the status of their latest order.” The assistant reads the email, looks up the customer, checks the order and prepares a draft. The person reviews it and sends it. Each server exposes only what is needed, with read-only access to the CRM and order system.
Adopting MCP in your business
- List the use cases: which tasks should the AI carry out, in which applications?
- Inventory the interfaces: existing MCP servers, available APIs, services with no interface.
- Define permissions: for each exposed tool, who may use it, read or write, with or without approval.
- Build or configure the servers, starting with read-only functions, which carry less risk.
- Test in real conditions on a limited scope, with a full log, before opening up more widely.
We support this approach in our AI integration projects: choosing the right access route, building MCP servers, hardening them and putting them into production.
Frequently asked questions
Is MCP only for Anthropic products?
No. It is an open standard with a public specification and SDKs in several languages, used by applications and models from different providers.
Do you need to be a developer to use MCP?
To use an existing server in a compatible application, no: it is often a matter of configuration. To expose business software that has no MCP server, yes: that is development work, usually modest if the software has an API.
Does MCP make AI riskier?
MCP makes AI more capable, so mistakes can carry more weight. Used well, it also improves control, since every access goes through identified, limited and logged points.
Does my data pass through MCP to the model provider?
The results of the tools called are sent to the model so it can use them. So keep exposed data to the strict minimum and, for sensitive information, anonymise it before it is sent.
Step By Step, a software and AI studio based in Ajaccio, Corsica, builds MCP servers and secure AI integrations. Let’s talk about your tools.