The Case Against MCP: Why LLMs Should Adapt to the World, Not Vice Versa

The Model Control Protocol (MCP), introduced by Anthropic, is pitched as a universal interface for large language models (LLMs) to interact with the external world—a “USB-C port” through which LLMs can connect to tools, APIs, and systems. At first glance, this standardization seems practical, promising seamless integration for AI in diverse applications. However, a closer examination reveals MCP as a flawed and potentially unnecessary solution. Rather than empowering LLMs to navigate the complex digital ecosystem as humans do, MCP imposes a simplified intermediary layer that reflects current AI limitations and risks becoming obsolete. This essay argues that LLMs should leverage their inherent adaptability to engage directly with existing tools and APIs, drawing on an analogy to self-driving cars and insights from ongoing AI advancements.

The Promise and Pitfalls of MCP

Anthropic describes MCP as a standardized protocol that enables LLMs to interact with external systems securely and efficiently. Much like a USB-C port allows a computer to connect to various peripherals, MCP aims to streamline how LLMs access databases, APIs, or software tools. For developers, this abstraction could simplify integration, ensuring LLMs operate within a controlled, predictable framework. In scenarios like enterprise workflows or casual chat-based interfaces, MCP’s uniformity might reduce complexity, offering a plug-and-play experience for non-technical users or organizations prioritizing security.

However, the need for a dedicated protocol raises a fundamental question: why should LLMs require a bespoke interface when they are designed to mimic human-like reasoning? LLMs, with their ability to generate code, parse documentation, and adapt to diverse contexts, are uniquely suited to interact with the digital world as it exists. Creating a new protocol like MCP feels akin to building bridges to connect every tool and API to LLMs—an inefficient and unsustainable endeavor. The digital ecosystem is vast and fragmented, with countless APIs, formats, and standards. Standardizing this chaos for LLMs is as impractical as rewriting the internet to be AI-friendly.

Moreover, MCP underscores a deeper issue: the current limitations of LLMs. These models rely heavily on context windows, which provide shallow, transient knowledge rather than deep domain expertise. MCP compensates for this by presenting tools in a simplified, standardized format, but this approach is inherently restrictive. It assumes LLMs cannot handle the complexity of real-world interfaces, requiring humans to pre-package the world for AI consumption. This is ironic—LLMs are heralded as problem-solvers, yet MCP suggests they need coddling to function effectively.

An Analogy: Self-Driving Cars and Infrastructure

To illustrate MCP’s flaws, consider the development of self-driving cars, which offers a striking parallel. One approach to autonomy involves creating a standardized communication system between vehicles and roads—smart infrastructure with sensors, signals, and vehicle-to-infrastructure networks. This would require overhauling global traffic systems, an undertaking so costly and complex that it’s widely deemed unviable. Instead, the prevailing approach embeds intelligence within the vehicle itself. Using cameras, radar, and AI, cars interpret existing roads, signs, and traffic patterns as humans do, adapting to the world without demanding its redesign.

MCP mirrors the less practical autopilot approach. By creating a standardized protocol, it attempts to retrofit the digital world for LLMs, much like building smart roads for cars. This imposes a heavy burden on developers to adapt tools to MCP’s framework, risking a walled garden where only MCP-compatible systems are accessible. In contrast, LLMs could emulate the vehicle-side intelligence model, relying on their code-generation and reasoning capabilities to navigate APIs, parse documentation, and execute tasks directly. Just as vision-based self-driving cars have proven more scalable, LLMs that adapt to the existing digital ecosystem will likely outpace MCP’s constrained approach.

The Case for LLM Autonomy

The alternative to MCP is clear: empower LLMs to function as autonomous agents within the current digital landscape. Imagine an LLM operating in a virtual machine with internet access, capable of browsing documentation, downloading tools, generating code, compiling it, and executing it. Such an agent would not need a simplified protocol—it would learn to call APIs, handle errors, and adapt to new systems, much like a human programmer. This vision aligns with emerging trends in AI. As of 2025, agentic systems—successors to tools like AutoGPT—are demonstrating the ability to perform complex workflows, from querying REST APIs to debugging scripts, without relying on standardized intermediaries.

This approach leverages LLMs’ strengths: their versatility and adaptability. Unlike humans, LLMs can process vast documentation instantly, generate tailored code on demand, and iterate rapidly. Requiring a protocol like MCP to mediate their interactions undermines these capabilities, forcing LLMs into a rigid framework that limits their potential. For example, in “vibe coding”—casual, exploratory programming—MCP might enable quick prototypes via chat interfaces. But for serious applications, such as building production-grade software, generating native code that directly calls APIs is far more effective. Native code avoids MCP’s overhead, offering precision and flexibility that a standardized protocol cannot match.

Counterpoints and Limitations

Proponents of MCP might argue that standardization enhances security and reliability. Without a controlled interface, LLMs freely interacting with APIs could introduce risks, such as executing malicious code or misinterpreting complex systems. In enterprise settings, MCP’s uniformity could simplify auditing and compliance, ensuring AI actions are traceable. Additionally, current LLMs are not fully autonomous—they struggle with long-term planning and edge cases, like ambiguous API documentation or rate limits. MCP might serve as a pragmatic stopgap, enabling LLMs to be useful today while their reasoning improves.

These points have merit, but they don’t outweigh MCP’s flaws. Security concerns can be addressed through sandboxed environments or robust error-handling, not by simplifying the world for LLMs. The stopgap argument also falters when we consider the pace of AI advancement. As LLMs evolve toward deeper reasoning—potentially integrating reinforcement learning or persistent memory—MCP’s role will diminish. A protocol designed for today’s limitations risks obsolescence tomorrow, much like early autopilot systems that relied on lane markers gave way to vision-based AI.

The Path Forward

MCP is a transitional technology, a reflection of LLMs’ current adolescence. It excels in niche scenarios, like conversational tools for casual users, but falls short for the ambitious goal of general AI. The digital world is too complex to be abstracted into a single protocol, and LLMs are too capable to be confined by one. Instead, we should invest in LLMs that emulate human programmers: browsing the web, learning from documentation, and crafting solutions tailored to real-world systems. This approach not only maximizes LLMs’ potential but also aligns with the ethos of AI—to augment, not constrain, human ingenuity.

The self-driving car analogy reminds us that intelligence at the edge—whether in a vehicle or an LLM—scales better than reengineering the world. Just as roads remain unchanged while cars grow smarter, the digital ecosystem should stand as is, with LLMs adapting to its diversity. MCP may offer short-term convenience, but the future belongs to autonomous AI agents that navigate the world with the same dexterity as their human counterparts. By betting on LLMs’ adaptability, we can unlock their true potential, rendering protocols like MCP relics of a less ambitious era.