The number of AI-powered applications is growing rapidly, and so are the subscriptions required to utilize them. It feels like every new tool demands its own "AI credits" or monthly fee, leaving developers and businesses managing a complicated and expensive stack.

A proposed solution to this fragmentation has been quietly circulating in engineering circles: the Model Context Protocol (MCP). On the surface, it's a brilliant idea for creating a more open and connected AI environment.

Yet, despite a flurry of development and discussion, MCP hasn't taken off (yet). If it's the answer to subscription fatigue and siloed AI, why isn't anyone actually using it?

What Exactly is the Model Context Protocol?

MCP is a standard that enables AI models to access external tools and information. Think of it as a common language that allows a large language model (LLM) to communicate with any external service, from a weather API to an internal company database, without requiring a custom-built integration for each one.

The main benefit, which is often overlooked in technical documentation, is straightforward: you can subscribe to a powerful base model, such as one from OpenAI, Google, or Anthropic, and then use MCP "connectors" to grant it new abilities. Instead of paying for a dozen separate AI services, you would pay for one and enhance it with tools that speak the MCP language. This would fundamentally change the economics of using AI, making one subscription way more capable.

On the left, an LLM needs a separate, unique integration for every tool. On the right, the LLM connects to MCP, which then communicates with all compatible tools through a universal standard.

This approach would threaten the business model of many specialized AI tools. If your primary model could instantly gain the ability to analyze PDFs, schedule meetings, or search the web via MCP, the need for standalone apps that do only those things would diminish significantly.

3 Reasons for MCP's Slow Start

The logic behind MCP seems sound, so its lack of adoption isn't because the idea is flawed. Instead, it's a mix of communication failures, market dynamics, and a classic adoption stalemate.

1. The Message is Lost in Technical Jargon

The most significant barrier to MCP adoption is how it has been explained. Most of the available information is aimed at protocol developers, not the end-users or application engineers who would benefit from it. Schemas, data formats, and API specifications dominate the conversation.

This has a chilling effect. The simple, powerful value proposition of "pay for one AI, connect many tools" gets lost in the engineering jargon. Without a strong "what's in it for me," most engineers will stick with the tools they already know.

2. The "Good Enough" Problem of Walled Gardens

The major AI platform companies are not waiting for a universal standard to emerge. They are building their own integrated ecosystems, often referred to as "walled gardens," and making them incredibly easy to use.

OpenAI has its GPT Store and Assistants API, which allows models to call predefined functions.
Google has integrated function calling in its Gemini models and connects them to the wider Google ecosystem through Vertex AI.
Anthropic also provides robust function-calling capabilities for Claude.

These proprietary solutions work very well and come with excellent documentation, tutorials, and support. For a developer working on a deadline, using a built-in, well-supported feature from a major provider is a much safer and faster choice than adopting a new, independent protocol with a small community. The convenience of these single-provider solutions is often "good enough" to prevent most from seeking an alternative.

3. The Chicken-and-Egg Dilemma

Any open standard faces a network effect problem, and MCP is no exception. For the protocol to be valuable, it needs widespread adoption on two fronts:

Tool Providers: Developers of APIs and services need to make their tools compatible with MCP.
Model Providers: The creators of LLMs need to build native support for MCP into their models.

This creates a stalemate. Tool developers have little reason to invest time and resources in supporting MCP if no major models can use it. In fact, some of the few public MCP servers available, like the one documented by APXML, are rumored to be exceedingly quiet places. Likewise, model providers like OpenAI or Google have little incentive to add MCP support if few tools use the protocol. Neither side wants to make the first move, and so the ecosystem never reaches the critical mass needed to become self-sustaining.

Conclusion

The Model Context Protocol is built on a compelling idea: creating an open, interoperable system for AI that saves money and increases capability. It represents a future where users are not locked into a single company's set of tools.

However, its progress is stalled by a failure to communicate its core benefits, fierce competition from the convenient walled gardens of big tech, and the classic adoption hurdle that all new standards face.

The need for AI interoperability isn't going away. As the AI market matures, the demand for models and tools that work together will continue to grow. While MCP in its current form may not be the final answer, the principles behind it will undoubtedly shape the next generation of AI development.

Why Is No One Using Model Context Protocol (Yet)