What is the Model Context Protocol (MCP)?

MCP was introduced in November 2024 by Anthropic (the company behind Claude). The protocol is now supported by OpenAI, Google and Microsoft. In December 2025, management was transferred to the Agentic AI Foundation under the Linux Foundation, making MCP officially a vendor-neutral standard.

The problem that MCP solves.

AI models know a lot about the world, but nothing about your organization. They have no access to your internal systems, your documents or your ongoing projects. Every question you ask about your own situation requires you to first manually provide context: look up the right data, copy, paste and reformulate the question.

That doesn't scale. And the alternative (employees copying company data to external AI tools) is a privacy risk. MCP solves both problems. It gives AI models controlled, standardized access to your systems. You determine which data is visible, which actions are allowed and who can use it.

How MCP works.

MCP follows a client-server architecture with three components:

MCP Host: the environment in which the AI model runs. This can be an AI assistant like Claude Desktop, but also an IDE like Cursor or an automated workflow in for example n8n.

MCP Client: the component within the host that translates the AI model's requests to the MCP protocol and sends responses from the server back to the model.

MCP Server: the layer that sits on top of your systems. Here you define three things:

• Tools: actions that the AI model may perform. For example: creating an invoice, opening a ticket or generating a report.

• Resources: data that the model may consult. Think of product lists, client data or documentation.

• Prompts: preset templates that help the model execute specific, recurring tasks.

The AI model discovers which tools and resources are available in each session and decides when it makes sense to deploy them. That is the fundamental difference with a traditional API integration: with an API you program in advance what should happen, with MCP the model determines that itself based on the question.

The difference between MCP and an API integration.

An API integration is static. You program in advance: if input X comes in, send output Y to system Z. That works fine for structured, predictable data flows. An order that goes from your webshop to your ERP, for example.

MCP is dynamic. The AI model determines itself which tools it needs based on the question being asked. It discovers what is available, weighs what is relevant and acts on it. That behavior cannot be captured in a fixed API definition.

The two do not exclude each other. In most organizations they work alongside each other: API integrations for structured data flows, MCP as an access layer for AI models.

What is WebMCP?

Google and Microsoft proposed an additional standard in 2025: WebMCP (Web Model Context Protocol). Where MCP focuses on server-side integrations with internal systems, WebMCP makes it possible for websites to declare their capabilities as structured tools for AI agents on the web.

WebMCP rests on three pillars: discovery (an AI agent can request which actions a page supports), JSON schemas (exact definitions of expected input and output) and state awareness (tools become dynamically available based on the status of the page).

WebMCP is still in an early phase and sits in Chrome 146 behind an experimental flag. Broader browser support is expected during 2026. For organizations starting with MCP now, it's good to know that this browser standard is coming, but it doesn't change anything about the current implementation of server-side MCP.

Security and compliance.

MCP is designed with security as a starting point. The protocol supports:

• Authentication: via OAuth or API keys. Every connection is verified.

• Permissions per tool: you set per tool which data is visible and who has access. A marketing AI doesn't need to see HR data, a support bot doesn't need to process financial data.

• Rate limiting and input validation: to prevent abuse or unintended excessive use.

• Audit logging: every interaction between the AI model and your systems is traceable. That is relevant for organizations that fall under GDPR or work with personal data.

Important: an MCP server runs on your own infrastructure. Data does not leave your environment unless you explicitly allow it.

MCP in combination with a Data Management Platform.

A Data Management Platform (DMP) ensures that data from different systems is centralized, structured and usable. MCP is the layer that subsequently gives AI models access to that data. They complement each other. The DMP handles the data foundations and business logic. MCP handles the access layer for AI. By placing MCP as a module on an existing middleware layer, you work with one central point instead of re-integrating per application.

Don't have a DMP yet? Then an MCP integration can also be built directly on separate systems.