Generative AI only unlocks its real value when it interacts with everyday business tools: CRM, helpdesk, calendar, DMS. This is exactly where the Model Context Protocol (MCP) comes in. Introduced by Anthropic at the end of 2024, MCP standardizes the connection between AI applications and third-party systems. The goal: faster integrations, secure action execution, and measurable business value.
This article explains MCP for beginners, provides practical examples, and highlights its limitations.
Table of Contents
What Is MCP?
MCP (Model Context Protocol) is an open standard that enables AI applications to interact in a structured way with external systems and tools via MCP servers — reading and writing data and executing workflows or functions. The actual ‘intelligence’ remains within the AI model; MCP provides the standardized bridge to external capabilities.
Why Everyone Is Talking About MCP
Companies and employees do not want to simply chat with AI — they want to automate work: create notes in the CRM, open helpdesk tickets, send emails, trigger workflows.
Instead of building every integration individually, MCP offers a standardized way to address these actions. This accelerates pilot projects and reduces integration effort — provided that the target systems expose a suitable interface.
Tools in the AI Context (Tool Calling)
Modern AI systems no longer rely solely on their internal “knowledge.” They also have access to various capabilities or functions they can call. These might include search, image generation, web research, or interaction with an MCP server.
Likewise, an MCP server itself is equipped with specific capabilities — for example:
- Triggering a workflow
- Requesting calendar access
- Modifying data in a system
How MCP Works in Practice
A typical process looks like this:
- A user request is submitted via chat or workflow.
- The AI model plans the solution and selects the necessary “tools.”
- The MCP server calls the appropriate capability in the target system — for example, search, modify data, or trigger a workflow.
- The result or confirmation flows back into the AI application — the user sees success or the next steps.
Practical Example
After a customer meeting, a meeting note should automatically be created in the CRM.
The AI generates the note based on the transcript. MCP uses the CRM capability “Create meeting note,” writes the information into the system, and returns “Successfully saved.”
Without MCP, this would require manual copy-paste work. With MCP, the manual task is automatically delegated and executed.
Strengths: Actions in Third-Party Systems — Fast and Standardized
The greatest advantage of MCP is its ability to perform real actions:Write values, Send emails, Trigger workflows, Create tasks etc. – this reduces media breaks, saves time, and makes processes more robust. Even complex tools can be addressed via a unified interaction logic — from CRM systems to image editing — provided an MCP interface is available.
Additional Use Cases
- Helpdesk Flow: Automatically open a ticket, enrich it with information, and trigger a customer email upon completion — AI handles routine tasks, humans review subject-matter decisions.
- Calendar/CRM: Check availability, confirm appointments, generate agenda suggestions — and store notes in the CRM.
- Procurement: Validate a purchase request, start an approval workflow, trigger the order in the ERP system.
- Accounting/Finance: Create a booking in the financial system based on an incoming invoice.
- Production/Operations: Read machine status from MES, create a maintenance order in CMMS, trigger spare parts ordering in ERP, and automatically archive the quality report in QMS after completion.
In short: MCP reduces manual effort, standardizes integrations, and accelerates automation — especially for recurring tasks.
Limitations of MCP
Many providers currently refer to MCP as a “panacea” due to its simplicity. However, this is a fallacy and demonstrates a lack of understanding of the technology, because not every capability that can be mapped via an MCP is actually useful or the best solution.
One example of this is searching for information. Consider the following situation: A SharePoint MCP has the ability to search. This means that it uses the SharePoint search in the background to retrieve information from SharePoint. The SharePoint search often does not deliver sufficient quality results. In practice, this means that the next step is not based on the best overall information, but on the first piece of information that comes up. This means that the AI makes wrong decisions because it was unable to find the right information. In this article we explain the limitations of MCP in detail.
In such a case, it would be much more advantageous to provide the AI system with the ability to perform high-quality searches using enterprise search. With increasing levels of automation, high-quality search is becoming increasingly important in AI systems. The difference between completing a 10-step process with an error rate of 95% or 99%:
| Steps | System with 95% accuracy | System with 99% accuracy |
| 1 | 95% | 99% |
| 2 | 90% | 98% |
| 3 | 86% | 97% |
| 4 | 81% | 96% |
| 5 | 77% | 95% |
| 6 | 74% | 94% |
| 7 | 70% | 93% |
| 8 | 66% | 92% |
| 9 | 63% | 91% |
| 10 | 60% | 90% |
It quickly becomes clear that search quality makes a massive difference.
If you want to dive deeper into this topic, download our white paper on MCP vs. index-based approaches. In 10 pages, we explain the technical approaches available for providing information to AI agents:
Deploying MCP in Established IT Infrastructures
To successfully implement this new technology, several aspects should be considered:
- Combine architectures: Use index-based search for knowledge questions and consistent retrieval. Use MCP to execute follow-up actions in systems. This maintains high answer quality while still automating processes.
- Maintain consistent access control: Apply the least-privilege principle both on the intermediary layer and in the target system. Always log write actions transparently.
- Ensure data protection compliance: Process personal data in compliance with GDPR. Secure sensitive actions (e.g., sending emails) with approval steps.
- Hosting and standards: For production scenarios, prefer EU/Germany hosting and ISO 27001-compliant processes — especially when MCP-based automations handle customer data.
Conclusion
MCP works very well for executing actions in third-party systems. It reduces integration effort, standardizes workflows, and efficiently automates routine processes.
The search should be index-based to ensure retrieval quality—especially for large data volumes, strict access models, and RAG applications that require reliable answers.
In short: combine both approaches. Use the index to find precise information and MCP to execute the next process steps directly in the specialist system. This keeps quality and speed high – and makes AI truly capable of action.
Do you still have questions? Then send a request to our team now and we will get back to you as soon as possible: