Did Anthropics end painful AI-integration?

“Model Context Protocol” (MCP): a closer look

One year ago, AI company Anthropic released its “Model Context Protocol” (MCP). The open-source protocol is designed as a standardized interface between AI models and data sources, helping AI applications access external data and tools more efficiently. After one year of MCP, TRUSTEQ draws a preliminary conclusion. What speaks for integrating the protocol — and what speaks against it?

The benefits are clear

First, the idea behind MCP is strong. Anthropic is tackling a problem every company faces when integrating AI tools into an existing IT landscape: every data source needs its own interface, every tool has its own formats and authentication logic — repetitive work that consumes time and resources.

A real-world example: A company wants to deploy three AI assistants — one for customer service, one for internal knowledge search, one for data analysis. All three need access to SAP, Confluence and the internal database. Using classic methods, developers still need to build a separate integration for each route: 3 AI tools × 5 data sources = 15 individual integrations.

A huge effort that slows innovation and frustrates developers.

The USB-C cable of AI models

The Model Context Protocol aims to end this. It creates a standardized “language” both sides understand. A one-size-fits-all solution — similar to what USB-C did for smartphones. Every device once needed its own connector; cable clutter was guaranteed. Today almost every device works with the same oval plug.

With MCP, it works like this: The data source becomes an MCP server, enabling any MCP-compatible AI system to access it. Suddenly, 15 individual integrations turn into 8 standardized components: 5 MCP servers and 3 clients.

The Pros

That isn’t just cleaner — it also saves significant resources:

Instead of months of custom development, MCP servers for common enterprise systems can be reused. Since the protocol is open-source, the MCP community is already building servers for popular platforms — from databases to CRM systems to internal wikis.

MCP shortens AI rollout cycles dramatically. Instead of building integrations for every pilot, teams can plug new applications into existing MCP servers. Experiments that once required weeks of coordination and development can be deployed within days. This speeds up innovation.

MCP is an open standard and ideally works with any AI model — whether ChatGPT, Claude or Gemini. Companies can switch AI providers without rebuilding their integration architecture. Vendor lock-in becomes a thing of the past — a valuable flexibility in today’s environment.

Security and compliance requirements are easier to enforce with MCP. Developers can implement access rights, audit logs and data-protection controls at the MCP server layer rather than in every single integration. That simplifies IT security processes and reduces vulnerability risks.

It sounds almost too good to be true. The main drawback is the protocol’s early stage. Expect these hurdles:

The Cons

Growing pains: The number of production-ready servers for enterprise systems is still limited, and best practices are only beginning to emerge. Anyone adopting MCP now is an early mover — with all the pros and cons that brings.

Cost: Transitioning to MCP requires upfront investment. Existing integrations don’t have to be replaced immediately, but new projects may require rethinking the architecture. Medium-term, these investments are likely to pay off.

Skills: IT teams need to learn the new standard. That requires training and possibly external support in the early stages — a challenge for already stretched teams.

Security: Rebuilding data-flow interfaces requires adjusting zero-trust architectures and security policies. This isn’t a disadvantage of MCP per se, but a necessary step for effective migration.

Who’s joining in? MCP’s success depends on broad adoption. USB-C only became useful because even Apple eventually joined in. As of now, Anthropic is the strongest supporter of MCP. Other major AI providers are watching but not yet committing. Whether MCP grows from niche solution to industry standard will become clear in the coming months.

Conclusion: A standard with potential

The Model Context Protocol tackles a real problem and offers substantial benefits for early adopters. Risks lie in the protocol’s youthful stage and uncertain buy-in from major players.

Still, the direction is right. Standardization has always accelerated technological innovation — and AI integration is overdue for such a standard. Companies that explore MCP early position themselves for a more flexible, cost-efficient AI future. And even if MCP doesn’t become the final standard, engaging with standardized integration approaches will pay off regardless.

If you’d like to explore how MCP could work in your organization, the TRUSTEQ AI team is available for non-binding consultations.

What’s your view? Does MCP look like a relevant standard for your company — or are you betting on different integration approaches?

Dr. Lennart Alexander van der Goten

Senior AI Consultant

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