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

In the past couple of years as AI systems have become more capable of not just generating text, but taking actions, making decisions and integrating with enterprise systems, they have come with additional complexities. Each AI model has its own proprietary way of interfacing with other software. Every system added creates another integration jam, and IT teams are spending more time connecting systems than using them. This integration tax is not unique: It’s the hidden cost of today’s fragmented AI landscape.

Anthropic’s Model Context Protocol (MCP) is one of the first attempts to fill this gap. It proposes a clean, stateless protocol for how large language models (LLMs) can discover and invoke external tools with consistent interfaces and minimal developer friction. This has the potential to transform isolated AI capabilities into composable, enterprise-ready workflows. In turn, it could make integrations standardized and simpler. Is it the panacea we need? Before we delve in, let us first understand what MCP is all about.

Right now, tool integration in LLM-powered systems is ad hoc at best. Each agent framework, each plugin system and each model vendor tend to define their own way of handling tool invocation. This is leading to reduced portability.

MCP offers a refreshing alternative:

• A client-server model, where LLMs request tool execution from external services;
• Tool interfaces published in a machine-readable, declarative format;
• A stateless communication pattern designed for composability and reusability.

If adopted widely, MCP could make AI tools discoverable, modular and interoperable, similar to what REST (REpresentational State Transfer) and OpenAPI did for web services.

Why MCP is not (yet) a standard

While MCP is an open-source protocol developed by Anthropic and has recently gained traction, it is important to recognize what it is — and what it is not. MCP is not yet a formal industry standard. Despite its open nature and rising adoption, it is still maintained and guided by a single vendor, primarily designed around the Claude model family.

A true standard requires more than just open access.  There should be an independent governance group, representation from multiple stakeholders and a formal consortium to oversee its evolution, versioning and any dispute resolution. None of these elements are in place for MCP today.

This distinction is more than technical. In recent enterprise implementation projects involving task orchestration, document processing and quote automation, the absence of a shared tool interface layer has surfaced repeatedly as a friction point. Teams are forced to develop adapters or duplicate logic across systems, which leads to higher complexity and increased costs. Without a neutral, broadly accepted protocol, that complexity is unlikely to decrease.

This is particularly relevant in today’s fragmented AI landscape, where multiple vendors are exploring their own proprietary or parallel protocols. For example, Google has announced its Agent2Agent protocol, while IBM is developing its own Agent Communication Protocol. Without coordinated efforts, there is a real risk of the ecosystem splintering — rather than converging, making interoperability and long-term stability harder to achieve.

Meanwhile, MCP itself is still evolving, with its specifications, security practices and implementation guidance being actively refined. Early adopters have noted challenges around developer experience, tool integration and robust security, none of which are trivial for enterprise-grade systems.

In this context, enterprises must be cautious. While MCP presents a promising direction, mission-critical systems demand predictability, stability and interoperability, which are best delivered by mature, community-driven standards. Protocols governed by a neutral body ensure long-term investment protection, safeguarding adopters from unilateral changes or strategic pivots by any single vendor.

For organizations evaluating MCP today, this raises a crucial question — how do you embrace innovation without locking into uncertainty? The next step isn’t to reject MCP, but to engage with it strategically: Experiment where it adds value, isolate dependencies and prepare for a multi-protocol future that may still be in flux.

What tech leaders should watch for

While experimenting with MCP makes sense, especially for those already using Claude, full-scale adoption requires a more strategic lens. Here are a few considerations:

1. Vendor lock-in

If your tools are MCP-specific, and only Anthropic supports MCP, you are tied to their stack. That limits flexibility as multi-model strategies become more common.

2. Security implications

Letting LLMs invoke tools autonomously is powerful and dangerous. Without guardrails like scoped permissions, output validation and fine-grained authorization, a poorly scoped tool could expose systems to manipulation or error.

3. Observability gaps

The “reasoning” behind tool use is implicit in the model’s output. That makes debugging harder. Logging, monitoring and transparency tooling will be essential for enterprise use.

Tool ecosystem lag

Most tools today are not MCP-aware. Organizations may need to rework their APIs to be compliant or build middleware adapters to bridge the gap.

Strategic recommendations

If you are building agent-based products, MCP is worth tracking. Adoption should be staged:

• Prototype with MCP, but avoid deep coupling;
• Design adapters that abstract MCP-specific logic;
• Advocate for open governance, to help steer MCP (or its successor) toward community adoption;
• Track parallel efforts from open-source players like LangChain and AutoGPT, or industry bodies that may propose vendor-neutral alternatives.

These steps preserve flexibility while encouraging architectural practices aligned with future convergence.

Why this conversation matters

Based on experience in enterprise environments, one pattern is clear: The lack of standardized model-to-tool interfaces slows down adoption, increases integration costs and creates operational risk.

The idea behind MCP is that models should speak a consistent language to tools. Prima facie: This is not just a good idea, but a necessary one. It is a foundational layer for how future AI systems will coordinate, execute and reason in real-world workflows. The road to widespread adoption is neither guaranteed nor without risk.

Whether MCP becomes that standard remains to be seen. But the conversation it is sparking is one the industry can no longer avoid.

Gopal Kuppuswamy is co-founder of Cognida. 

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