Model Context Protocol (MCP) – Definitely a milestone for AI integration, but what are the limitations?
9 minutes read
MCP – the next evolutionary stage in AI usage
Artificial intelligence (AI) has undergone rapid development in recent years. Once confined to laboratories and research facilities, it has now become an integral part of everyday business life in the form of chatbots, automated analyses, and digital assistants. However, despite impressive advances in generative AI, many companies face a fundamental challenge: How can AI be linked to existing data, systems, and processes in a meaningful, secure, and scalable way? This is exactly where MCP comes in – not only as a strategic enabler that allows generative AI to be used to answer questions, but also to execute real business processes – context-aware, modular, and interoperable.
In this blog article, you will learn:
- What MCP is and how it works
- Which specific application scenarios exist
- Why MCP is so important for future-oriented companies
- Where the limitations of MCP are
- How MCP and knowledge graphs are interlinked
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard that allows AI models such as ChatGPT, Claude, or LLaMA to retrieve and use context-related information from real-world enterprise systems in a structured and secure manner. It was developed to bridge the gap between powerful language models and the complex, dynamic IT landscape of enterprises.
A key problem with conventional AI systems is that they often operate in an “information vacuum”: they are unaware of a company's internal data, documents, processes, and tools. The result: inappropriate, incorrect (hallucinated) responses. MCP addresses this issue by providing a standardized communication channel that connects the model to relevant resources, tools, and prompts.
MCP offers several practical advantages that are particularly beneficial in complex business environments:
Context awareness
AI can better understand and process tasks, queries, or texts because it can access company-specific information and knows where to find which information.
Operational capacity
AI can not only respond, but also perform actions – e.g., schedule meetings, create reports, or analyze data.
Modularity
The underlying systems (data sources, tools, processes) remain untouched because MCP acts as a mediation layer that connects AI and the system world in a modular way – without additional integration hurdles or vendor lock-ins.
To understand this, here is a brief look at the MCP architecture.
The three main components:
1. Host: The AI system or application (e.g., Claude, ChatGPT, IDEs, assistance systems)
2. Client: The MCP-compatible component that mediates between the host and server
3. MCP Server: The central interface that enables access to:
- Resources (e.g., files, databases)
- Tools (e.g., calendar, CRM, Jira, email)
- Prompts (predefined interactions)
Interaction between these components is standardized via the JSON-RPC protocol – a lean, text-based protocol for remote procedure call communication (i.e., “call function X with parameters Y”).
An exemplary process:
A production manager asks his company's internal AI (e.g., via an assistance system with a chat interface): “Which production line had the highest reject rate in the last three weeks, and which were the most common causes?”
The AI (host) analyzes the query and recognizes that external information is necessary—such as machine sensor data from the MES system, quality reports from the QMS, and shift data from the HR system (context awareness). Thanks to MCP, it can access, analyze, and aggregate this information.
It then generates a structured query via MCP to the client.
The MCP client receives the request and identifies which systems are needed to answer it:
- MES (Manufacturing Execution System) → Rejection rates per line
- SCADA / sensor data → Machine statuses and error codes
- QMS (Quality Management System) → Cause analyses, test reports
- HR system → Shift data (when, which teams)
The client then sends requests to these systems via existing APIs or data adapters, taking into account:
- Host access rights (e.g., the AI is only allowed to view lines 3 to 5)
- Data protection (e.g., no personal information from HR)
- Transformation logic to bring all data into a uniform format
Neither the MES, HR, nor QMS systems need to be modified. The document management system and calendar also remain unchanged (modularity).
The systems deliver their data, the MCP server aggregates it, and generates a standardized response.
The AI (host) receives this structured response and uses it to generate understandable, natural-language feedback: “Line 4 had the highest reject rate in the last three weeks at x%. The main causes were temperature deviations in machine X and an undetected miscalibration of the dosing arm. There was a noticeable increase in the late shifts of weeks 28 and 29. Further details can be found in the latest quality audit report.”
Upon request, the AI stores the generated report in the desired format in the organization's internal document management system and creates a calendar entry for a meeting with all relevant persons (capacity to act).
Examples of MCP application in practice
Although MCP is designed as an industry-agnostic standard, it really demonstrates its value in environments where there are many heterogeneous data sources, complex processes, and high requirements for security, traceability, or regulation. In such environments, MCP enables the structured, secure, and context-aware integration of AI systems into existing IT and process landscapes. The following examples from industry, life sciences, and the energy sector illustrate how MCP can become the foundation for practical, productive AI applications in demanding business contexts.
Manufacturing:
In manufacturing, MCP improves efficiency, quality, and maintenance through focused AI support:
- Access to manufacturing data and machine sensors for monitoring ongoing processes
- Automatic creation of quality reports based on real-time data
- Predictive maintenance through AI analysis of machine conditions and error codes
Pharmaceuticals & Life Sciences:
In regulated industries such as the pharmaceutical industry, MCP helps to structure data and automate processes:
- Analysis of clinical studies and literature databases
- Summary of regulatory requirements (e.g., EMA, FDA)
- Coordination of processes between research, production, and documentation
Power & Utility Management:
In the energy sector, MCP helps to manage complex technical and regulatory requirements
- Collection and evaluation of consumption, grid load, and weather data
- Optimization of energy management and grid planning
- Automated reporting to authorities, e.g., in the format of EN IEC 81346 for structuring technical systems
These industry-specific scenarios show that MCP brings AI directly into the value chain – not as a black box, but as an active, controllable agent.
Benefits of MCP for Enterprises
- Scalability: New tools and data sources can be easily integrated without having to retrain the AI model.
- Security: Access rights, authorisation, and data flows are transparent and controllable.
- Faster time-to-value: Instead of months of integration, it often takes only a few hours or days.
- Standardisation: Uniform interfaces facilitate maintenance, audits, and governance.
- Vendor neutrality: MCP works with different AI models, cloud providers, and software landscapes.
- Sustainability: No more ‘disposable code’ – MCP integrations are reusable and future-proof.
Sounds amazing? Of course!
Despite the many advantages offered by MCP, it should not be forgotten, though, that MCP just connects AI with data. In accordance with the old but still valid saying ‘garbage in, garbage out’, even the best integration will be ineffective without clean and understandable semantic data.
MCP + Knowledge Graphs: Context, Structure, and Semantics
To better understand the concepts of 'clean', 'understandable' and 'semantic' data, let's take a brief look at knowledge graphs. A powerful AI system needs one thing above all: context. This is where knowledge graphs enter the picture. They structure corporate knowledge in the form of entities, relationships, and rules – machine-readable, comprehensible, and interconnected.
What do knowledge graphs do?
Unlike isolated data sources, knowledge graphs connect content semantically. Instead of just storing knowledge, they make it findable, accessible, understandable, interoperable, and reusable – for humans and machines alike. Example: ‘Machine A is part of line B’ is not just text in the graph, but a relational statement – embedded in an overall structure.
You want to learn more about the real value of enterprise knowledge graphs? We recommend reading this article about Symbolic AI.
How MCP and KG work together
MCP establishes the connection – knowledge graphs provide the content. This means that:
- MCP can integrate knowledge graphs as a structured data source
- AI can ask specific questions about entities, facts, or contexts
- Results are more context-rich, structured, and easier to explain
- The combination promotes explainability and trustworthiness
Only a good foundation can create real added value: reliable, explainable, and scalable AI, embedded in existing business processes. MCP is the solid bridge, but the quality of the answers depends on the knowledge that is integrated. If the data is incomplete, unstructured, or incomprehensible, the AI result will also be unusable – regardless of the interface.
Reliable, trustworthy AI requires semantics
For AI to perform reliably and be trustworthy, it requires more than just standardised access. It requires:
- Up-to-date, well-maintained knowledge
- Orchestration of subgraphs from different systems
- Rights and access controls
- Standardised ontologies and taxonomies
In other words, up-to-date, interconnected, secure knowledge.
As already mentioned, knowledge graphs offer a fantastic opportunity here. Depending on the platform, knowledge integration via a knowledge graph can even be partially automated and mastered with little or no data expertise. A concrete example of this is eccenca Corporate Memory: a knowledge graph platform that enables semantic knowledge management as a no-code/low-code solution, as well as AI-supported, automated knowledge integration. In addition, it already has an integrated MCP server, which greatly simplifies the connection of your own enterprise knowledge graph to external AI systems.
What does MCP combined with a semantic data layer mean for the fitness of your company
Companies that want to use AI in the long term must be able to ensure the scalability, security, maintainability, and governance of their IT and data infrastructure.
MCP acts as a mediation layer between generative AI models and the complex, dynamic data and tool landscapes in companies. It creates the conditions for AI to not only provide selective answers but to actually be integrated into existing workflows and decision-making processes. Therefore, MCP needs to be part of the infrastructure for a sustainable, productive AI infrastructure within the company.
But MCP does not unleash its full potential on its own. It is the combination of a standardised access protocol (such as MCP) with access controls and structured, integrated, and maintained data and knowledge sources (such as provided by a semantic knowledge graph) that allows you to build AI solutions that
- support trustworthy enterprise AI agents
- enable reliable automation of complex processes
- leverage governance and explainability in regulated industries
- are scalable across the enterprise and can be continuously developed
- allow reasoned decision-making.
Conclusion
Just as USB-C provides a common connector for laptops, mobile phones, and chargers, MCP provides a uniform connector for connecting AI to the corporate world. In a world where AI models no longer just respond, but act, integrate, and automate independently, MCP is more than just a protocol – it is an enabler for productive AI use, a bridge builder between models and data, as well as an accelerator for innovation. Investing in MCP-compatible architectures in combination with a semantic data layer ensures that your AI solutions are future-proof, expandable, and truly value-adding.
As a provider of a knowledge graph platform with integrated MCP support, we empower organisations to transform their knowledge into semantically enriched, linked, AI-ready data – bridging the gap between data, processes, and generative intelligence.
Would you like to find out how your company's knowledge can be made MCP-compatible – or how easy semantic data integration can be?
We would be happy to advise you.
