MCP: a standard, not a product.
The Model Context Protocol (MCP) is an open standard that describes how an AI model connects to external systems: databases, CRM, ERP, files, APIs. Anthropic (the company behind Claude) published it as open source in November 2024. In 2025, OpenAI and then Google DeepMind adopted it, followed by most of the ecosystem. It has become the de facto standard: thousands of MCP servers exist today, both official and community-built, for most tools on the market.
The analogy people use is the right one: MCP is to AI what USB-C is to hardware. Before USB-C, every device had its own proprietary charger. Before MCP, every connection between an AI model and a tool was a custom build, to redo for every model and maintain forever. The standard replaces that multiplication with a single plug.
Important point for a business leader: MCP isn't a product to buy. It's a free technical convention that your systems and your AI tools either speak or don't. The question isn't "should we buy MCP" but "is my infrastructure ready to use it."
From M × N integrations to M + N.
The problem MCP solves is arithmetic. Take 3 AI use cases (an internal assistant, a qualification agent, a reporting agent) and 4 systems (CRM, database, invoicing, documents). Without a standard, every use case has to be wired to every system: 12 integrations to build, test, maintain, and redo if you switch AI models.
With MCP, each system exposes one MCP server (once), and each AI use case is one MCP client (native in modern tools). Result: 3 + 4 = 7 building blocks instead of 12 wired connections, and every new block immediately benefits from all the others.
| Before MCP | With MCP | |
|---|---|---|
| Integrations | One per tool × model pair | One per tool, period |
| Switching AI models | Rewire everything | Nothing changes on the tools' side |
| New AI agent | Redevelop access from scratch | Reuses existing servers |
| Maintenance | Every connection ages on its own | One maintained server serves every use case |
| Access governance | Scattered across every integration | Centralized per server |
That last point, governance, is what makes MCP a leadership topic and not just a technical one: AI's access to your systems becomes something visible, listable, revocable. You know who can read what.
How it works, without unnecessary jargon.
Three roles, that's it. The host is the AI application you use (an assistant, an agent, an internal tool). The MCP client is the connector built into that host. The MCP server is the small program that exposes one of your systems. The server can run locally or be accessed remotely over HTTP, with authentication.
An MCP server exposes three things to the model:
- Tools: actions the model can trigger. "Find a customer," "create an invoice," "update a status."
- Resources: data the model can read. The contents of a folder, a table, a document.
- Prompts: ready-to-use instruction templates, defined by you, to scope recurring use cases.
How it plays out in practice, take an SMB example: you ask, "put together the client update for Dupont." The agent discovers the available tools via its MCP servers, calls "find customer" on the CRM server, reads the latest invoices via the invoicing server, queries the support ticket database, then writes the summary using real, current data. What used to take four browser tabs and twenty minutes now happens inside the conversation.
The beauty of it: the model discovers the capabilities on its own. Every server describes its tools in a standard format, no need to re-explain to each model what it can do. It's this discoverability that sets an MCP server apart from a plain API.
What MCP changes for an SMB.
Behind the protocol, four very concrete benefits.
Your AI infrastructure becomes a reusable asset. Every MCP server you build (your customer database, your catalog, your invoicing) serves every present and future use case. The second agent costs less than the first, the third even less: the access already exists.
You're no longer locked into one AI model. Claude today, another tomorrow, a European model for certain use cases: the MCP servers don't move. Choosing a model becomes a reversible decision again, made on the criteria that matter (see our comparison Which AI to Choose in 2026).
AI's access finally gets governed properly. One server equals one scope equals a set of permissions. The reporting agent reads the database but doesn't write to it; the HR assistant doesn't see accounting. This kind of scoping, impossible to enforce with scattered integrations, becomes the default architecture.
Your AI agents gain depth. An agent connected to fresh, complete data reasons correctly; an agent without access makes things up or stays generic. MCP is the plumbing that feeds real context to 24/7 AI agents.
And the prerequisite hasn't changed: clean, structured, accessible data. An MCP server connected to a messy database exposes mess. Making your data usable by AI agents is exactly the subject of our 2026 Playbook.
MCP in action, inside an SMB.
Querying your database in plain language
A read-only MCP server on your data warehouse, and the business owner asks: "Revenue by segment this quarter, compared to last year?" The model queries the database, calculates, answers with real numbers. The next question costs nothing: no more waiting for Monday's report.
An assistant that knows your customers
MCP servers on the CRM, invoicing, and support conversations. Before every meeting, the assistant compiles the complete profile: history, outstanding balance, open tickets, latest interactions. Meeting prep goes from twenty minutes to a single question.
An agent that acts inside your systems
The level above: the document-processing agent reads the incoming invoice, checks the supplier in the ERP via MCP, pushes the entry into the accounting tool, and flags any discrepancy. Write actions go through explicit, scoped, logged MCP tools.
These three levels get built in order, each one relying on the servers from the one before. It's the exact illustration of our "collect, transform, activate" model: MCP lives at the boundary between the last two.
Connecting AI without opening the floodgates.
An MCP server is a door into your data. The standard provides the mechanisms, but deployment is what makes it secure. The rules we apply on every installation:
- One server equals one minimal scope. The server exposes only the capabilities the intended use case needs. Read-only by default; write access is an explicit decision, tool by tool.
- Authentication and dedicated accounts. Every server runs with its own revocable credentials, never with a human's account. Remote servers require strong authentication.
- Full logging. Every tool call is traced: who, what, when, with what data. Essential for auditing and for improving.
- Review third-party servers before connecting them. The community ecosystem is rich; anything that touches your production data deserves a code review or a server maintained by an identified vendor.
- Separate environments. An agent gets tested on test data. Production comes once the behavior is validated.
The standard is young and evolving fast; that's an argument for scoped, monitored deployments, not for waiting. Companies building their first MCP servers properly now will have the infrastructure ready by the time their competitors start asking the question.
The sensible sequence to get started.
Inventory your data and systems
Where does your data live, in what state, with what APIs? That's the basic mapping, and the core of our 2026 Playbook. Without it, you're connecting to disorder.
Expose one high-leverage source
Just one, read-only: usually the customer database or the data warehouse. An existing MCP server if your tool has one, a lightweight build if not. The goal is one useful first use case, not total coverage.
Launch a measured pilot
One use case, identified users, one metric (time saved, questions resolved). Four to six weeks is enough to know whether the use case holds up and what to adjust before expanding.
Expand by value
Every new server gets added where the gain is measurable, and every agent reuses what already exists. That's how AI infrastructure gets built: in useful layers, not in one big bang.
That's exactly our job as an AI & Data agency: design this infrastructure end to end, deploy it, and operate it for the long run. Our production systems already use MCP to connect agents and data; the detail of our disciplines is on the Services page.
No promises. No jargon. A precise diagnosis, clear architecture, delivered on time.
R. Thiébaut, CTO, SaaS Scale-up
MCP: what people ask us.
What is MCP in one sentence?
MCP is an open standard that lets an AI model connect to your tools and your data (CRM, databases, ERP, files) through a single interface: it's often described as the USB-C port of AI. A tool exposed once via MCP becomes usable by all your assistants and AI agents, whatever model is behind them.
Who created the MCP protocol?
Anthropic (the company behind Claude) published MCP in November 2024 as an open, open-source standard. It was then adopted by the other major players, including OpenAI and Google DeepMind in 2025, which made it the de facto standard for connecting AI models to external systems. The ecosystem today counts thousands of MCP servers.
What's the difference between MCP and an API?
An API exposes a tool's functions, but every AI integration has to be custom-coded for each API. MCP is a standard layer on top of APIs: it describes tools and data in a format every AI model understands natively, with automatic capability discovery. You connect once on the tool's side, and every compatible AI client benefits.
What's the difference between MCP and RAG?
RAG is a technique: retrieving the relevant passages from a document base to ground the model's answer. MCP is standard plumbing: the way the model accesses sources and tools. The two combine: a RAG system can be exposed as an MCP server, and an agent that queries your documents via MCP is doing RAG. Neither replaces the other.
Is MCP ready for production use in a business?
Yes, provided you deploy it with the security practices it requires: authentication on every server, minimal permissions (read-only by default), logging of every call, review of third-party servers before connecting them. The standard is young and evolving fast, which argues for scoped, monitored deployments rather than connecting everything at once.
Do you need to build your own MCP servers?
Not always. MCP servers already exist for common tools: databases, Google Drive, Slack, GitHub, and most major SaaS platforms. Custom development becomes relevant for your internal systems (in-house ERP, proprietary database, internal API): it's a lightweight build that exposes exactly the capabilities you want to give AI, with your own access rules.