Model Context Protocol (MCP): definition, value and limits
Model Context Protocol Updated
Definition
The Model Context Protocol (MCP) is an open standard through which an AI model accesses external tools, data and systems. Instead of programming every connection individually, the model speaks to one uniform connector that exposes systems like CRM, ERP or a database. For process automation this means less custom glue between AI and existing systems.
Why the Term Matters
The effort in AI projects often sits not in the model but in connecting it to existing systems. MCP standardises this connection, so a model uses the same interface no matter which system is behind it. In the mid-market this lowers integration cost and makes flows more maintainable. Knowing the term, you plan connections cleanly once instead of hand-building them per use case.
When It Is Relevant
- An AI flow needs access to several internal systems.
- Connections should be reusable, not rebuilt per project.
- Tools and data sources change while the AI logic should stay the same.
- Access must run in a controlled and traceable way.
When Not
When a flow touches only a single system that never changes, the standard is hardly worth it. And when no AI is involved and a classic point-to-point integration is enough, MCP is the wrong tool. A lean direct interface stays simpler then.
Industries That Use It
- IT and software: make internal tools and databases available to AI flows through one uniform connector.
- Logistics: AI reaches transport, warehouse and order systems through a standard, without a special solution per system.
- Financial services: controlled access to core and posting systems with clear logging.
- Manufacturing: connect machine and order data for AI assistance without maintaining every interface separately.
Typical Use Cases
- An AI assistant reads order data from the ERP through a standardised connector.
- Several data sources are exposed for one flow without individual wiring.
- A tool is swapped out while the AI logic stays unchanged.
- Access runs through defined permissions and is logged.
- A pilot connects one system by standard and is later extended without a rebuild.
Decision Guide
| Signal | MCP fits | Alternative |
|---|---|---|
| AI needs many systems | one uniform connector | point-to-point for a single system |
| Reuse connections | standard interface | individual integration per project |
| Tools change often | separate AI logic from connection | hard wiring |
| No AI in the flow | classic integration | MCP not needed |
Example
A software house builds an internal AI assistant that pulls order and customer data from several systems. Instead of programming each connection separately, it exposes the systems through a standardised connector. When a ticketing system is added later, only the new connector is plugged in and the AI logic stays the same. This describes the method, not an asserted client metric.
Service Connection
We plan connecting AI to SAP, CRM and internal systems in process automation with AI. How much standardisation a case truly needs is clarified in the process automation consulting.
Sources
This framing describes common practice for connecting AI to existing systems. Concrete implementation, permissions and data protection depend on the case, the systems and the tools and must be checked case by case.