In the rapidly evolving landscape of artificial intelligence, the interaction and application of large models have become pivotal to technological progress. Recently, a new standardized interaction protocol known as MCP (Model Calling Protocol) has captured significant attention across the global tech community. From industry titans like OpenAI and Google to Anthropic, major AI players are embracing MCP, aiming to achieve seamless interaction between large models through this protocol. The question on everyone's mind: could MCP truly become the "HTTP of AI"?

At its core, MCP addresses a fundamental tension within AI development, particularly the evolution of Prompt Engineering. While prompts guide AI models, integrating external tools and data – crucial for building capable AI Agents – traditionally requires developers to manually manage complex technical interfaces and adapt to different models' proprietary Function Call mechanisms. This is akin to needing a different adapter for every single device you own. MCP revolutionizes this by abstracting large model interactions into a familiar "client-server" architecture, much like how the internet works. Any AI application (MCP Host) supporting the protocol can configure and use an MCP Server from an application market, offering a "plug-and-play" experience similar to connecting a USB device. This dynamic capability to call specific modules as needed allows models to instantly access relevant context and flexibly expand their abilities.

The limitations of vendor-specific Function Call implementations resemble the predicament caused by proprietary fast-charging protocols for mobile phones. Major AI vendors define their own closed calling protocols, forcing developers to duplicate efforts for different platforms. Shifting AI service providers means rebuilding the entire tool calling system, significantly hindering the scaling of AI capabilities. MCP acts as an "AI's USB-C interface," standardizing disparate Function Calling standards. By defining unified communication and resource standards (like JSON-RPC 2.0), MCP allows developers to build an interface just once, and it can be called by almost all major models. This is the AI world's equivalent of standardizing writing systems or currency, ending the need to "reinvent the wheel" repeatedly.

While Function Call remains efficient for high-frequency, lightweight tasks, acting like a model's "close assistant", MCP excels at "outsourcing complex tasks," functioning more like an "on-demand response courier". This shift moves from a "code-control" mindset, where developers micro-manage tool details, to an "intent-driven" mode, where developers define capabilities and the model dynamically decides execution. This doesn't signal an immediate overthrow of Function Call but rather a push for its evolution towards more standardization.

The impact of MCP is far-reaching. It dramatically lowers the technical barrier to AI application development, making the vision of "everyone being an AI developer" more tangible by decoupling AI development. For AI vendors, the focus shifts from adapting to specific tools to ensuring protocol compatibility – MCP is the "universal socket" allowing models to access a vast ecosystem of tools. Tool developers gain a "develop once, full ecosystem compatible" advantage; encapsulating a function as an MCP Server means it can be used by any compatible AI application, driving rapid AI adoption across products. For application developers, MCP dissolves technical boundaries, accelerating the transition from Graphical User Interfaces (GUI) to Language User Interfaces (LUI). Just as the iPhone's touch screen fundamentally changed human-computer interaction, MCP's unified function calling standard positions natural language as the ultimate interface connecting user intent with system capabilities.

However, challenges remain. The debate around MCP's potential highlights concerns about agent accuracy when integrating new tools, the current need for local service deployment (raising the usage threshold), lack of server-side deployment capabilities for scale, and unresolved security issues like authentication (though planned). The path to becoming a "de facto standard" is a "battle of patterns," acknowledging that the outcome is not yet definitive.

MCP's emergence signifies a new phase in AI development: ecological competition. Like the HTTP protocol laid the foundation for the internet, MCP is potentially building the "digital nervous system" for the intelligent era. Its value lies not just in the technical specification but in fostering an open collaboration paradigm where models, tools, and data can flow freely under a unified protocol. While MCP's ultimate dominance is uncertain, it undeniably brings us closer to Artificial General Intelligence by standardizing the language of AI-tool interaction.

（Writer：Tick）