Recently, Anthropic launched MCP (Model Context Protocol), attracting considerable attention from the tech community. Officially described as the "USB-C of the AI world," MCP attempts to align itself with USB-C's universal and open characteristics. However, upon closer examination, this analogy doesn't hold up. In reality, MCP more closely resembles a proprietary USB-C dongle, where the "C" firmly stands for Claude.
First, let's clarify what MCP actually is. MCP is an open-source initiative from Anthropic, intended to assist developers in building AI applications based on the any LLM model. Anthropic emphasizes that MCP is under an MIT open-source license, implying broad openness. Yet in practice, the openness is superficial. While the license technically permits free use and community modification, almost all of the platform's key examples and official documentation are tightly bound to Anthropic's own APIs. Documentation remains notably unclear, particularly around vital aspects such as system prompt engineering and tool discovery.
Why does MCP fail to align with the USB-C analogy? USB-C is an authentic open standard promoted jointly by IEEE and the open-source community, genuinely implementable by any vendor. MCP, despite its open-source claim, heavily relies on proprietary interfaces from Anthropic's Claude model. Thus, the proclaimed openness is surface-level at best, creating subtle but significant barriers within the ecosystem.
Historically, Anthropic has exhibited a cautious, even resistant stance towards openness. The company has yet to publicly disclose Claude’s underlying architecture or its command-line tools, often citing "security" as justification for secrecy (though the actual reasons are broadly understood). Within this context, MCP's sudden "openness" feels more strategic than authentic. Yet, despite these strategic intentions, MCP has undeniably gained significant traction.
The primary reason is straightforward: Claude 3.5's impressive performance. Apps like "Lovable," previously little-known, became highly popular after Claude's upgrades—not due to innovative product development but by leveraging the technological advancements of Claude itself. Similarly, tools like Cursor have deeply integrated Claude into their functionalities. This technological strength naturally motivates developers, as integrating MCP effectively equates to tapping into larger user bases—a benefit that is hard to resist.
Nevertheless, this arrangement carries inherent risks:
Security concerns are evident and require little additional commentary.
Sustainability is another critical issue: Without long-term organizational commitment from Anthropic, how trustworthy and maintainable is the platform?
Moreover, MCP imposes considerable technical barriers for developers. Proficiency with Python, Node.js, or Docker is essential, and the installation and setup process is far from straightforward. This further underscores the dongle analogy. If MCP were truly the USB-C equivalent, it would represent the seamless future of software development, effortlessly integrable right from the start. Clearly, MCP falls short of this ideal.
In conclusion, despite claiming to be the "USB-C of AI," MCP functions more like a proprietary USB-Claude dongle—dependent, with ugly Python or Node deps (like a dongle).
Unless Anthropic genuinely addresses openness, ecosystem sustainability, and transparency in security, MCP's potential remains capped. It might find popularity among developers as an appealing tool for enhancing platforms like Cursor or Claude desktop, but its broader influence will inevitably remain limited.
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