What is the MCP protocol?
The Model Context Protocol (MCP) is an open communication standard that defines how AI models access external systems. Developed by Anthropic and published as an open standard, MCP solves a fundamental problem of AI integration: until now, every connection between an AI model and a business system had to be developed individually. MCP standardises this communication.
Technically, MCP is based on JSON-RPC 2.0 — a lightweight, proven messaging protocol. The architecture follows a client-server model: the AI agent acts as the client, and the MCP server establishes the connection to your systems.
The comparison that fits best: MCP is to AI integrations what HTTP is to the web. Before HTTP existed as a standard, every server could provide data in its own way. HTTP created a common language — and with it the web as we know it. MCP does the same for the world of AI agents.
The three core concepts of MCP
The MCP protocol revolves around three central building blocks. Each has a clear job:
-
check_circle
Tools: Functions the AI agent can actively execute. Examples: “Create a new customer in the CRM”, “Send an email”, “Update the project status”. Tools are actions — they change something in your systems. Each tool has a description that explains to the AI model when and how to use it.
-
check_circle
Resources: Data the AI agent can read. Examples: customer master data, product catalogues, stock levels, contract documents. Resources are passive — they supply context and information without changing anything. The AI agent can use them to give well-founded answers.
-
check_circle
Prompts (templates): Predefined interaction patterns that the MCP server provides to the AI agent. Examples: a template for creating quotes, a scheme for customer-call summaries, a format for status reports. Prompts ensure that AI outputs are consistent and company-specific.
The combination makes the difference: Only the interplay of the three concepts unfolds the full effect. An AI agent can read a resource (retrieve customer data), execute a tool (create a quote) and follow a prompt (adhere to the company standard for quotes) — all in a single work step, without a human having to intervene.
Why MCP changes everything
To understand why MCP is so significant, it helps to look at the situation before MCP:
Imagine you wanted to connect your AI agent to your CRM. For that you needed a developer who knows the API of your CRM system, understands the quirks of the AI model and builds a custom integration. Want to connect your ERP as well? Another custom integration. Your email system? Another one. And if you wanted to switch the AI model — from GPT to Claude, for example — you might have had to rebuild everything.
The result: N AI models times M systems meant N x M custom integrations. With 3 AI models and 5 systems, that was 15 separate integrations, each of which had to be developed, tested and maintained.
With MCP the arithmetic changes fundamentally: every system needs one MCP server. Every AI model needs one MCP client. 3 AI models and 5 systems make 3 + 5 = 8 components instead of 15. At larger scale the difference becomes even more dramatic.
But it is not only about efficiency. MCP enables true interoperability: an MCP server developed for Claude automatically also works with GPT, Gemini and any other MCP-compatible model. Investments in MCP servers are therefore future-proof — regardless of which AI model proves to be the best tomorrow.
Who already uses MCP?
The speed at which the industry has adopted MCP is remarkable. As of March 2026, these platforms support the MCP protocol natively:
- Anthropic (Claude) — invented MCP and supports it in all Claude products, including the API, Claude Desktop and Claude Code
- Google (Gemini) — full MCP support in the Gemini API and Google Cloud services
- OpenAI (GPT, ChatGPT) — MCP client integration in the API and the Assistants
- Microsoft (Copilot) — MCP support across the Copilot platform and Azure AI Services
- Cursor — the popular AI code editor uses MCP as its primary integration layer
- Windsurf, Cline, Zed — further developer tools with native MCP support
In addition, the ecosystem of prebuilt MCP servers is growing rapidly. For many common systems — from Slack and GitHub to databases such as PostgreSQL — open-source MCP servers already exist. For company-specific systems, custom MCP servers are developed.
The MCP protocol in practice: 3 company scenarios
Theory is good, practice is better. These three scenarios show how the MCP protocol is used in real company environments:
- Sales automation A mid-sized company connects its AI agent via MCP servers to Pipedrive (CRM), the internal product database and the email system. The sales rep says: “Create a quote for Meier Ltd. for 500 units of item X with the usual discount.” The AI agent retrieves the customer data from Pipedrive (resource), gets the current price and availability from the database (resource), calculates the discount, creates the quote from the company template (prompt) and sends it by email (tool). What used to take 20 minutes is done in 30 seconds.
- Document processing in purchasing Incoming supplier invoices are read by the AI agent. Through an MCP server it accesses the ERP system, matches the invoice against open purchase orders, checks prices and quantities and files the invoice in the document management system. In case of discrepancies, it automatically creates a clarification note for purchasing. The entire invoice check runs in seconds instead of minutes — with an error rate close to zero.
- BI reporting for management The AI agent is connected via MCP servers to the SQL database, the CRM and the project management tool. The managing director asks in the morning: “How are we doing this week?” The agent analyses revenue figures, pipeline status, open projects and team utilisation — and delivers a compact briefing report in natural language. No clicking through dashboards, no opening Excel sheets, no requesting reports.
MCP server vs. MCP client: understanding the architecture
In the MCP architecture there are two clearly separated roles:
The MCP client is the AI agent’s side. It makes requests, calls tools and processes the results. In most cases the MCP client is already built into the AI model or its framework — you do not have to develop it yourself. Claude, GPT and Gemini come with their own MCP clients.
The MCP server is your company’s side. It knows your systems, provides tools and resources and controls access. The MCP server is the component that is developed individually for your company — because it has to know your specific systems, data structures and business rules.
An AI agent can be connected to several MCP servers at the same time: one for the CRM, one for the database, one for the email system. From the agent’s point of view, these are simply different “toolboxes” it draws on — depending on what the current task requires.
Important to understand: The MCP server is the gatekeeper. It decides which data and functions are available to the AI agent. A poorly configured MCP server can lead to data leaks or unintentionally give the agent write access to critical systems. That is why careful configuration of access rights is not an optional feature but a basic requirement.
Security and data protection with the MCP protocol
For German companies, GDPR compliance is not optional but mandatory. The MCP protocol brings properties out of the box that support data protection:
Authentication and authorisation
MCP servers support authentication at protocol level — the AI agent must identify itself before it gets access. In addition, granular authorisation can be implemented: different agents or user roles can see different tools and resources. The customer service agent sees different data than the sales agent.
Least privilege and access control
A professionally configured MCP server follows the principle of least privilege: the AI agent only gets access to the data and functions it needs for its specific task. Read and write access are controlled separately. Critical actions (such as deleting records) can be given approval workflows.
EU hosting and DPA
The MCP server runs in your infrastructure or on EU servers you trust. Your business data does not leave the EU. In combination with a data processing agreement (DPA) with the AI provider, you meet the central GDPR requirements. You can find more in our article on GDPR-compliant AI implementation.
Logging and traceability
Every interaction between AI agent and MCP server can be logged without gaps: which agent accessed which data, and when? Which actions were executed? These logs are important not only for GDPR compliance but also for the continuous optimisation of your AI processes.
The path to MCP integration: 4 steps
What does the concrete path look like if you want to use MCP in your company? We have organised the process into four clearly defined phases:
- Analysis: which systems and processes? In the first step, we identify which of your systems should be connected via MCP and which processes benefit. Not every process needs AI — and not every system needs an MCP server. Before you set up an MCP server, we prioritise together by impact and effort and create a clear roadmap.
- Architecture: MCP server design Based on the analysis, we design the MCP server architecture: which tools and resources does each server need? Which access rights apply? How is authentication handled? Where is it hosted? This phase produces the technical concept that takes all security and data protection requirements into account.
- Development: implementation and tests The MCP servers are developed, integrated into your existing infrastructure and tested intensively. We work iteratively: first connect one core system, validate it in practice, then expand step by step. That minimises risk and lets us deliver tangible results early.
- Monitoring: operations and optimisation After go-live, we continuously monitor the MCP servers: performance, error rates, usage patterns. The logs show us which tools are used most, where the AI agent reaches its limits and where there is room for optimisation. That way the integration keeps getting better over time.
Further reading
MCP integration for your company?
In the free initial consultation, we analyse how the MCP protocol can accelerate your AI strategy — GDPR-ready and with a clear roadmap.
Book an AI Check arrow_forwardFrequently asked questions
What is the MCP protocol?
The MCP protocol (Model Context Protocol) is an open standard that defines how AI models communicate with external systems. It is based on JSON-RPC and follows a client-server model. MCP was developed by Anthropic and lets AI agents access tools, data sources and business systems in a standardised way.
Is the MCP protocol an open standard?
Yes, the MCP protocol is a fully open standard. Anthropic has made the specification publicly available, and anyone can develop MCP servers and MCP clients. Google, OpenAI, Microsoft and numerous other companies have adopted the standard, making MCP the de facto standard for AI integrations.
How secure is the MCP protocol?
The MCP protocol supports authentication and authorisation at server level. The MCP server controls which tools and data are available to an AI agent. Combined with EU hosting, data processing agreements and granular access control, a GDPR-compliant MCP implementation can be put in place.