Model Context Protocol: A promising AI integration layer, but not a standard (yet)

Over the past few years, there has been additional complexity as AI systems can not only generate text, but also take actions, make decisions, and integrate with enterprise systems. Each AI model has its own way of interfacing with other software. All the added systems create separate integrated jams, and IT teams spend more time connecting systems than they use them. This integrated tax is not unique. The hidden costs of today’s fragmented AI landscape.

Anthropic’s Model Context Protocol (MCP) is one of the first attempts to fill this gap. We propose a clean, stateless protocol for how a large-scale language model (LLM) can discover and invoke external tools with consistent interfaces and minimal developer friction. This could potentially transform an isolated AI feature into a configurable, enterprise-ready workflow. Second, integration could be standardized and simpler. Is that the panacea we need? Before we dig deeper, let’s first understand what an MCP is.

Currently, tool integration in LLM drive systems is ad hoc at best. Each agent framework, each plug-in system, and each model vendor tend to define their own ways of handling tool invocations. This reduces portability.

MCP offers a refreshing alternative:

• A client-server model in which LLMS requests the tool to be run from an external service.
• A tool interface published in a machine-readable declarative format.
• Stateless communication patterns designed for complexity and reusability.

If widely adopted, MCPs could make AI tools discoverable, modular and interoperable.

Why MCP is not (yet) standard

MCP is an open source protocol developed by humanity and has recently gained traction, but it is important to recognize what it is and what it is not. MCP is not yet an official industry standard. Despite its open nature and increased adoption, it remains maintained and led by a single vendor designed primarily around the Claude model family.

A true standard requires more than just open access. An independent governance group, representatives from multiple stakeholders, and a formal consortium to oversee its evolution, versioning, and conflict resolution are required. These elements are not introduced for MCP today.

This distinction is more than technical. In recent enterprise implementation projects, including task orchestration, document processing, and citation automation, the lack of a sharing tool interface layer has repeatedly emerged as a friction point. Teams are forced to develop adapters across the system or develop overlapping logic across the system, leading to increased complexity and costs. Without a neutral and widely accepted protocol, it is unlikely to diminish its complexity.

This is particularly relevant to today’s fragmented AI landscapes where multiple vendors are investigating proprietary or parallel protocols. For example, Google has announced the Agent2Agent protocol, while IBM is developing its own agent communications protocol. Without coordinated efforts, there is a real risk of ecosystem division – making it difficult to achieve interoperability and long-term stability rather than converge.

Meanwhile, the MCP itself is still evolving, with its specifications, security practices and implementation guidance being actively refined. Early Adapters are paying attention to the challenges surrounding them Developer Experience, Tool Integration And solid safetynone of them are trivial to an enterprise-grade system.

In this regard, businesses must be cautious. MCPs are in a promising direction, but mission-critical systems are in demand for predictability, stability and interoperability that are best provided by mature, community-driven standards. Protocols managed by neutral organizations ensure long-term investment protection and protect employers from unilateral changes or strategic pivots by a single vendor.

This raises important questions for organizations assessing MCP today. How do you embrace innovation without actively locking in uncertainty? The next step is not to reject the MCP, but to engage strategically. Add value, isolate dependencies, and prepare for a multiprotocol future that could still be in flux.

Things Technology Leaders Should Look At

While MCP experiments make sense, full-scale adoption requires a more strategic lens, especially for those using Claude. Here are some considerations:

1. Vendor Lock-in

If the tool is MCP-specific and only humanity supports MCP, it is tied to the stack. As multimodel strategies become more common, flexibility is limited.

2. Security Impact

Calling LLM autonomously is powerful and dangerous. Without guardrails such as scoped permissions, output validation, and fine-tuned approvals, insufficient scoped tools can expose your system to operations and errors.

3. Observability gap

The “inference” behind the use of the tool is implicit in the output of the model. This makes debugging difficult. Logging, monitoring and transparency tools are essential for enterprise use.

Tool Ecosystem Rug

Most tools today are not MCP-AWARE. Organizations may need to re-create their APIs to ensure compliance or build middleware adapters to fill gaps.

Strategic Recommendations

If you’re building an agent-based product, MCP is worth tracking. Adoptions must be performed:

• This is a prototype using MCP, but avoid deep binding.
• An adapter that designs abstract MCP-specific logic.
• Advocates open governance and helps steer MCPs (or their successors) towards community recruitment.
• Track parallel efforts from open source players such as Langchain and AutoGpt, or from industry groups that may propose neutral alternatives to vendors.

These procedures maintain flexibility while encouraging architectural practices that are consistent with future convergence.

Why this conversation is important

One pattern is clear based on experience in an enterprise environment. The lack of standardized intermodel interfaces slows adoption, increases integration costs, and creates operational risks.

The idea behind MCP is that models need to speak a consistent language to the tool. PRIMA FACIE: This is not just a good idea, it’s a necessary idea. This is the foundational layer of how future AI systems coordinate, execute, and infer. The path to widespread adoption is not guaranteed and risk-free.

It is still unknown whether MCP will be the standard. But the conversations that spurred it is no longer inevitable for the industry.

Gopal Kuppuswamy is a co-founder Cognida.