Language models are impressive – but isolated by default. They know nothing about your customers, your stock levels or your calendar. For AI to be truly useful in a business, it needs controlled access to exactly that information and those tools. That is what the Model Context Protocol, MCP for short, is for.
MCP in one sentence
The Model Context Protocol is an open standard that lets AI applications communicate with external data sources and tools in a uniform way. Anthropic released MCP as an open specification in late 2024; since then it has been adopted across the industry – including by OpenAI, Google, Microsoft and numerous developer tools.
The problem MCP solves
Before MCP, every AI application had to build every integration on its own. Ten AI tools and twenty business systems could mean two hundred individual connectors in the worst case. With MCP, each system exposes its capabilities once as an MCP server – and every MCP-capable AI application can use them.
How MCP is structured
MCP distinguishes three roles:
- Host: the application people work with – such as Claude, ChatGPT, an IDE or your own agent.
- Client: the component inside the host that maintains the connection to a server.
- Server: a program that exposes a system’s capabilities – for example your CRM, ERP or file server.
An MCP server offers three kinds of building blocks:
- Tools – actions the AI may perform, such as “find invoice” or “create appointment”.
- Resources – data and content that serve as context, such as a document or a customer record.
- Prompts – ready-made templates for recurring tasks.
Local servers run directly on a computer and communicate via standard input/output (stdio). Remote servers run centrally and are accessed over HTTP (Streamable HTTP) – ideal for teams and companies. For remote servers, the specification builds on OAuth 2.1 for authorisation.
A real-world example
An employee asks her AI assistant: “Which customers have overdue invoices above €5,000?” The assistant recognises that it needs the list_invoices tool from the ERP MCP server, calls it with the right parameters, then fetches the matching contacts from the CRM server and prepares reminder emails as drafts. Nothing is sent until she approves. You can watch exactly this flow in the MCP playground on our home page.
Why MCP matters for businesses
- Build once, use everywhere: one server works with many AI clients – today and in the future.
- No vendor lock-in: you can switch AI models without rebuilding integrations.
- Control: you decide exactly which data and actions are exposed.
- Traceability: every tool call can be logged and analysed.
- New channels: software vendors can make their product available inside AI assistants through an official MCP server.
Security: what really matters
MCP is a protocol – how secure a solution is depends on the implementation. Key points: least-privilege permissions per tool, proper authentication, validation of every input, confirmation for critical actions and protection against prompt injection, i.e. manipulated content designed to trick an AI into unwanted actions. Also, only use MCP servers from trusted sources.
How to get started with MCP
- Pick a clearly scoped, high-value use case, such as read-only access to customer data.
- Define the tools and data you need – and what is explicitly off-limits.
- Build a lean server with the official SDKs and test it with real questions.
- Roll it out to a pilot group, measure usage and expand step by step.
Want to make your system AI-ready? We build MCP servers from concept to operations – just get in touch.