Model Context Protocol (MCP) for Bots und AI-Agents

Artificial intelligence and large language models (LLMs) have long been part of everyday life in contact centres and customer service. Companies do not tie themselves to a specific AI model, but flexibly use the best solution in each case – whether OpenAI, Google, Anthropic or local models. But while generic world knowledge dialogues quickly provide initial ‘aha’ moments, practice shows that real benefits only arise when bots can securely and efficiently access internal company data and processes. This is exactly where the Model Context Protocol (MCP) comes in – a standard that turns pure information into real, value-adding automation.

Link to the MCP webinar: https://www.youtube.com/watch?v=SNRAiTV-Bmw

From knowledge retrieval to transaction competence

Many bots fail in productive operation due to one central hurdle: they can answer FAQs or provide general information, but their competence ends when it comes to individual requests – such as contract changes, damage reports or status queries. Classic retrieval augmented generation (RAG) is no longer sufficient here because it only works for static knowledge. With MCP, transactions and dynamic processes can be standardised and securely integrated into the bot architecture for the first time.

In concrete terms, this means that a single, well-documented access point to the backends is sufficient to grant controlled access to a large number of bots – regardless of whether they come from OpenAI, Anthropic or Google. This saves companies a huge amount of integration effort and enables them to roll out innovations more quickly and with less risk.

MCP as the standard for smart automation

The Model Context Protocol (MCP) has established itself as a future-proof standard developed by Anthropic to bridge the gap between artificial intelligence and highly sensitive, dynamic enterprise systems. MCP is completely technology-agnostic: it does not tie any organisation to a specific LLM or proprietary solution, but opens the way for flexible innovation. This allows managers to access the most powerful technology at any time – such as AI models from OpenAI, Google, Anthropic or their own models.

Personalisation and real-time data access

MCP enables standardised access to static and dynamic company data for the first time – a milestone for personalised, process-driven customer communication. Whether contract data, status queries or transactions: everything is controlled, securely and transparently provided via the MCP server. Processes are no longer rigidly programmed, but controlled via prompts. This makes the entire solution flexible, quickly adaptable and maximally future-proof.

How MCP connects your systems with intelligent bots

The Model Context Protocol works like a ‘USB-C port’ for AI applications. There is a clear division of roles:

The bots/agents (or LLMs) conduct the customer dialogue, recognise intents, collect all necessary information and orchestrate the process.
The MCP client bundles all requests to the specialist systems and implements the security requirements.
The MCP servers provide the actual capabilities, which are divided into three classes:
- Tools for transactions (e.g. create claims, check account balance),
- Resources for structured data (e.g. contract documents),
- prompts for recurring instructions and work processes (e.g. contract summaries).

This structure allows even complex business processes to be automated in real time.

A practical example: insurance claims

Imagine a customer reporting the theft of her e-bike by telephone. The bot automatically recognises the issue, asks for all relevant details (contract number, date of theft, serial number) and initiates the transaction in the backend using the ‘create claim’ tool. If any information is missing, the MCP server makes specific enquiries or checks directly via the bot whether the claim is covered by the insurance policy. Within seconds, the customer receives consistent, traceable feedback – a service experience that traditional IVR systems cannot offer.

In addition to comprehensive self-services, MCP can also be used in the background of bots and agents for agent assistance, analytics and follow-ups.

Roadmap for implementation

How can you achieve a truly smart, future-proof customer experience with the latest technology?

Define use cases
Define data strategy
Use RAG for static data
Use MCP for dynamic (and static) data
Start small, scale big

MCP seamlessly integrated into our CreaLog bot platform

Support for large AI models is provided directly in the LLM, so that the latest developments can be integrated at any time. For maximum security and performance, CreaLog, as a platform provider, offers integration directly into its own bot platform. This means that all sensitive data and transactions remain in the protected corporate environment – crucial for data sovereignty and data sovereignty. Companies can choose whether to run MCP on-premises, in the cloud or in hybrid architectures. The data always remains on the MCP server and is only made accessible to authorised bots.

The advantage for you: The MCP client is directly and deeply integrated into the CreaLog bot platform. This means that it is used via the bot platform – not via the LLM itself. MCP clients and MCP servers communicate exclusively via local connections. The LLM can optionally be operated in the cloud, or smaller models can be integrated locally. The orchestration of all AI components remains technology-agnostic for speech recognition, speech synthesis and channel selection, so that companies can always choose the optimal solution.

Summary

The Model Context Protocol puts an end to isolated solutions and individual connections for individual bots. It professionalises and accelerates the integration of AI agents in contact centres and customer service. CreaLog brings together standards and expertise: in addition to innovative technology, companies receive tried-and-tested advice, high security and our experience from numerous use cases – from the initial idea to productive operation.

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