What is MCP? Model Context Protocol explained for business

In late 2024, Anthropic released an open standard called MCP (Model Context Protocol). Eighteen months later, MCP is supported by OpenAI, Google, Microsoft, and hundreds of other companies. It has quietly become the USB-C of AI: one connector for every system your AI model needs to talk to.

This article explains what MCP actually is, how it works, when it pays off for your business, what it costs, and how it compares to alternatives like one-off API integrations or RAG.

MCP (Model Context Protocol) is an open standard that securely connects AI models to external tools, data, and systems, without requiring a separate integration for every AI model and every system you use.

That makes MCP the integration layer between AI and the software you already run: your CRM, your document storage, your project management tool, your database, your internal wiki.

What is MCP and why does it exist?

Before MCP existed, every AI model lived in its own silo. Want ChatGPT to work with your CRM? Custom integration. Want the same thing with Claude? Another custom integration. Switch to Gemini? Start over.

The problem grew week by week. Companies had ten tools, five AI models, and no consistent way to connect them. Engineers wrote the same integration code, in slightly different shapes, for every combination.

MCP solves this with one simple rule: define the tool once according to the standard, and any AI model that supports MCP can use it.

In practice that means:

• Your CRM publishes one MCP server. Claude, ChatGPT, Gemini, Copilot, or any other model can use the same interface.
• Your AI assistant can read your project plan, send an email, open a ticket, or generate a report without custom glue code.
• Switch AI vendors? Your integrations keep working. No vendor lock-in.

Anthropic published the first version in late 2024. OpenAI officially adopted MCP in 2025. Microsoft built it into Copilot, Google into Gemini. That broad backing is exactly why MCP is already the de-facto standard for AI tool integrations.

How does MCP work technically?

MCP runs on three components that hold the same conversation: client, server, and transport.

The MCP client lives inside the AI model or AI application. Claude Desktop, ChatGPT, Cursor, and VS Code are all MCP clients. The client knows which MCP servers are available and routes requests accordingly.

An MCP server is a process that exposes one specific capability. A Slack MCP server offers tools like "read message," "send message," or "create channel." A Postgres MCP server offers "run query" or "read schema." A GitHub MCP server offers "open issue," "read pull request," or "merge branch." Every server publishes its own catalog of tools, prompts, and resources.

The transport is how client and server talk. For local tools that means stdin/stdout. For servers running on the network it's HTTP with server-sent events (SSE) or WebSocket. The AI model doesn't care which transport is used; the conversation always flows through the same JSON-RPC messages.

A typical flow looks like this:

1. The user asks the AI model: "Which open invoices does customer Acme have?"
2. The model recognizes it needs access to the accounting system and asks the MCP client for available tools.
3. The MCP client lists an Exact Online server with the tool "invoices.search."
4. The model invokes that tool with parameters: customer=Acme, status=open.
5. The MCP server queries Exact and returns the result.
6. The model phrases a readable answer using the invoices it found.

The whole cycle typically takes one to three seconds and is invisible to the user. For a broader explanation of how AI models use tools, read our post on what an AI agent is.

What can businesses do with MCP?

MCP only gets concrete when you look at what it enables for an SMB. A few real-world examples from Dutch business practice:

Customer service with full context. Your AI chatbot reaches into your CRM (customer history), order management (recent purchases), ticketing system (open issues), and knowledge base (manuals) through MCP servers. The agent gives answers that account for the specific customer, not generic boilerplate.

Sales support on demand. A salesperson asks in Slack: "Send a quote to Jan at Bakery De Tarwe for 500 units of variant B." The agent reads the CRM, checks pricing in the ERP, generates the quote as a PDF, emails it to Jan, and logs everything back in the CRM. No human work between the request and the sent quote.

Project management without context-switch costs. Your AI copilot reads tickets in Jira, time tracking in Toggl, documentation in Confluence, and emails in Outlook, all through MCP. The question "Which projects are off track and why?" gets answered using actual data, not gut feel.

Accounting assistant. Your accountant or staff member asks: "Which suppliers did we accidentally pay twice this month?" An MCP server above your accounting software runs the query and returns the answer, no spreadsheet diving required.

Code reviews and deployments. For development teams: an AI agent reads pull requests, runs tests, compares against the current main branch, writes change summaries, and can deploy after approval. MCP servers for GitHub, GitLab, and CI tools make this workable.

Document workflows. An MCP server above Google Drive or SharePoint gives your AI model selective access to folders. Ask "Summarize all new contracts from this week" and the agent delivers without manual uploads.

Inventory and logistics. An MCP server above your WMS or ERP lets the AI agent check stock levels, place orders, and plan shipments. For logistics and trading businesses without their own developers, this is one of the fastest paths to concrete time savings. See also our article on AI in logistics for related applications.

HR and onboarding. New hire? An agent with MCP access to your HR system, IT provisioning, and document tooling can run the whole welcome flow: create the account, assign permissions, send manuals, and book first meetings. Work that otherwise eats hours by hand.

MCP vs API integrations vs RAG

Three techniques get mentioned in the same breath: MCP, classic API integrations, and RAG. They solve different problems and are often complementary.

When do you choose what?

Choose MCP if your AI model needs to perform actions in another system or fetch live data the moment the user asks. Example: "Book this meeting in my calendar."

Choose a traditional API integration if your systems need to exchange data continuously without AI in the middle. Example: a new order in your webshop automatically creates an invoice in your accounting software.

Choose RAG if your AI needs to answer questions from your own documents and knowledge base, with source citations. Example: an internal assistant that searches manuals, policies, and old tickets.

In practice you often combine all three. An agent uses RAG for knowledge, MCP for actions, and underneath, API integrations handle the data flows between systems.

Which MCP servers already exist?

One reason MCP is growing fast is that the catalog of off-the-shelf servers is exploding. At the time of writing, more than 800 public MCP servers are available. The most-used categories:

Productivity and collaboration

• Slack, Microsoft Teams, Discord
• Notion, Confluence, Coda
• Google Workspace (Drive, Docs, Sheets, Gmail, Calendar)
• Microsoft 365 (Outlook, OneDrive, SharePoint)

Development

• GitHub, GitLab, Bitbucket
• Linear, Jira, Asana
• Sentry, Datadog, Grafana
• Postgres, MySQL, MongoDB, SQLite

Sales and marketing

• HubSpot, Salesforce, Pipedrive
• Mailchimp, ActiveCampaign
• Stripe, Mollie, Adyen

Finance and HR

• Exact Online, Twinfield, AFAS
• Personio, BambooHR
• DocuSign, SignRequest

Beyond public servers, you can build a custom MCP server for your own software. The MCP SDK is available in Python, TypeScript, Go, Rust, and Java. A simple server for your own API typically takes an experienced developer one to three days to build.

Security and governance with MCP

MCP gives AI models access to your real systems. Take that seriously. The biggest pitfalls we see in practice:

Permissions too broad. An MCP server that can "read all email" can also leak commercially sensitive information. Give AI agents access only to what is strictly required. A sales agent does not need your HR system.

No audit trail. Who called what, with which parameters, and what was the result? Without logging you can't tell what an agent did when something went wrong. Read our post on AI audit trails and logging for the essential controls.

Prompt injection through MCP. An MCP server that returns documents can unintentionally pass instructions through to your AI model that are hidden inside those documents. A malicious PDF can hijack your agent's behavior. Whitelist which MCP servers you trust and treat their output as untrusted user input.

No version control. MCP servers update. What was a safe read-only tool in version 1.2 might suddenly be able to write in 1.3. Pin versions explicitly and review changelogs.

Unsupervised tool use. An agent may invoke ten tools back-to-back in a single session. Without limits and approval gates for risky actions (transferring funds, emailing customers, modifying contracts), you're trusting business operations to a statistical model. Build human-in-the-loop checks for anything irreversible.

For businesses subject to the EU AI Act or operating in regulated sectors (financial, healthcare, legal), MCP governance isn't optional. You must be able to demonstrate which data went via which server to which model and on what basis a decision was made.

How do you start with MCP?

The simplest route in four steps:

Step 1: Pick one concrete use case. Not "MCP everywhere." Start with one pain point where AI access to an existing system delivers fast results. For example: an AI assistant for your sales team that can read the CRM.

Step 2: Check whether an MCP server already exists. For most popular SaaS tools one already does. HubSpot, Salesforce, Slack, Notion, Google Workspace: all available. No integration work needed.

Step 3: Configure and test in a small environment. Set up the server with the minimum permissions the use case needs. Give only sales access. Test against production data, but in read-only mode first.

Step 4: Scale step by step. Working? Add the next tool. Not working? Adjust prompts and server configuration before rolling out to the rest of the company.

For businesses without an in-house development team, the threshold is now genuinely low. Tools like Claude Desktop, ChatGPT Team, and Cursor offer graphical interfaces for adding MCP servers. For custom integrations with your own systems it's worth bringing in an AI agency experienced with MCP and your type of stack.

What does an MCP implementation cost? For a setup with three to five existing SaaS tools and standardized MCP servers, you typically spend €3,000 to €8,000 on one-time configuration, plus a few hundred euros per month in model API costs and hosting. For custom MCP servers above your own systems, expect €5,000 to €15,000 per server depending on complexity and the state of the underlying API. Ongoing costs are relatively modest: a few hundred euros per month for maintenance and a few tens to a few hundred in AI API tokens depending on usage.

When MCP is not the right choice

MCP is powerful, but not a silver bullet. Skip it or postpone it if:

Your systems have no API. MCP servers lean on the underlying API. Does your old ERP have no accessible API? Then you need to fix that underlying problem first, possibly via a legacy system AI integration layer. MCP becomes the last link, not the first.

Your use case is purely informational. Want the AI to only answer questions based on your documents? RAG is cheaper and simpler. MCP adds complexity you don't need.

Your security isn't ready. Does your business not yet have a permissions matrix, audit trail, or separation between staging and production? Then MCP introduces an attack surface you can't manage. Invest in IT hygiene first.

The use case happens once a year. MCP servers require maintenance. For one-off tasks, a single script is cheaper.

For businesses still figuring out whether MCP is the right choice, it pays to first test candidate applications against the broader palette of AI solutions. Our article on choosing the right AI solution helps with that.

MCP and multi-agent systems

A point that often gets skipped: MCP gets really interesting once you combine multiple agents. One agent reads data via MCP server A, a second agent performs actions via MCP server B, a third keeps oversight and corrects course. That's what we call multi-agent AI systems.

A concrete example: a sales orchestrator agent receives a lead. A research agent uses MCP to gather public data on the company. A writing agent uses an MCP server above your CRM to look up earlier contacts. A scheduling agent books the demo via an MCP server above your calendar. The orchestrator coordinates. The whole flow runs end-to-end without human intervention.

Without MCP, each of those agents would maintain its own integrations. With MCP, they share the same set of servers and exchange data via a common standard. Development time gets cut roughly in half.

Conclusion: why MCP matters now

In eighteen months, MCP has cleaned up the messy, fragmented world of AI integrations. For SMBs that means three concrete things:

Shorter implementation time. What used to be weeks of integration work in 2024 now often takes days. For popular tools, sometimes hours.

No lock-in to a single AI vendor. Your investment in MCP servers works with OpenAI, Anthropic, Google, Microsoft, and open-source models simultaneously. Switch models? Your integrations stay.

Easier to grow step by step. Start with one server, add another tomorrow. No more big-bang projects.

That said, MCP is no silver bullet. It's an excellent standard for AI tool access, but you still need a clear understanding of your processes, clean data, and tight governance. Whoever rolls out MCP without strategy just gets a messier AI architecture, faster.

Want to know which MCP applications save your business the most time at the lowest risk? Book a free automation scan and we'll walk through the best entry points together.