As AI technology develops rapidly, the Model Context Protocol (MCP) is becoming a new focus for technology giants. This standardized interaction protocol proposed by Anthropic in November 2024 aims to provide standardized interfaces for large models and clients, enabling them to efficiently and securely call external data sources and tools, thereby breaking through the static capability limitations of large models and providing a technical foundation and ecological support for Agents. With the entry of major companies such as Baidu, Alibaba, Tencent, and ByteDance, the ecological development of MCP is showing a booming trend. It not only reconstructs the development paradigm of AI applications, but also reshapes the competitive landscape of the entire technology industry.

MCP: A universal socket for AI applications

The emergence of MCP is analogous to the USB-C interface for electronic devices, providing a unified standard for connecting AI models with external tools. Under the traditional development model, AI integration tools need to be customized and developed. Tool calls for different large models follow different structures and parameter formats, and they cannot communicate with each other. Context management relies on manual maintenance by developers, and integration efficiency is low. The MCP protocol realizes "one-time packaging, global availability". After the server encapsulates the API according to the MCP standard, it can be called by all front-end clients that support MCP, greatly reducing access and operation and maintenance costs. It enables AI models to call various external tools and data sources conveniently, just like building blocks, thereby realizing more complex and intelligent functions.

Taking the e-commerce industry as an example, Baidu released the world's first MCP Server for e-commerce transactions, such as MCP and search MCP, which allows developers to easily call e-commerce related functions, such as product search and transaction processing, greatly improving development efficiency and the level of application intelligence. Similarly, in the field of content creation, through the MCP protocol, AI applications can quickly call tools such as text generation and image generation to achieve multi-modal content creation.

Competition among big companies: MCP ecosystem layout

Baidu: Technology first, promoting the implementation of MCP in multiple scenarios

Baidu's actions in the MCP field can be described as swift and powerful. At the Create AI Developer Conference in 2025, Baidu released the Wenxin Big Model 4.5 Turbo and Wenxin Big Model X1 Turbo, and announced that it would help developers fully embrace MCP. The e-commerce transaction MCP and search MCP launched by Baidu provide developers with a rich AI capability calling interface. For example, developers on the Baidu Smart Cloud Qianfan platform can directly add Baidu AI search results and Baidu's preferred MCP Server to the existing "Universal Intelligent Agent Assistant", allowing the intelligent agent to complete the entire process from recommending books to purchasing. In addition, Baidu Maps fully accessed the MCP protocol as early as March 21, becoming one of the earliest map applications compatible with MCP.

Alibaba: Building a closed business loop to seize the commanding heights of the ecosystem

Alibaba Cloud has demonstrated its consistent strategic vision and execution in building the MCP ecosystem. The launch of Alibaba Cloud Bailian MCP platform provides 50+ pre-set MCP services, attempting to build a complete business closed loop. Alipay, AutoNavi Maps and other Alibaba core applications have been connected to the MCP protocol, forming a powerful ecological synergy effect. Alibaba Cloud's MCP ecological platform not only serves its own business, but also provides external developers and enterprises with a wealth of AI tools and resources to help them quickly develop AI applications. Through MCP, Alibaba Cloud has further consolidated its leading position in the fields of cloud computing and AI, and strived to seize the ecological commanding heights in the AI ​​era.

Tencent: Focus on WeChat ecosystem and expand the boundaries of AI applications

Tencent Cloud focuses on the WeChat ecosystem and builds an AI application closed loop with MCP as the core. Tencent Cloud's TI platform supports MCP plug-in hosting, mainly for the WeChat ecosystem and payment tools. With the help of the MCP protocol, Tencent Cloud can integrate AI capabilities more closely into WeChat's social, payment and other scenarios, providing developers with more convenient tools and services. For example, through MCP, developers can easily integrate AI chatbots, smart recommendations and other functions in WeChat applets or official accounts to enhance user experience and application value.

ByteDance: Coze Space and MCP merge to create an intelligent platform

ByteDance's Coze Space is a rising star. It has built a powerful AI Agent platform by integrating the MCP protocol. Coze Space not only has basic functions such as intelligent processing of Excel tables and generation of simple PPT, but also can easily call various external tools such as Amap, VariFlight, Moji Weather, etc. through MCP extension to realize the automation of complex tasks. For example, users can command Coze Space to generate a detailed travel plan. It can quickly call relevant tools to generate a guide containing pictures of scenic spots, weather conditions, food routes, etc., showing strong intelligent capabilities.

Agent universal socket: connecting the physical world depends on AI network

Although MCP provides a powerful "universal socket" for AI Agents, enabling them to easily call various external tools and data sources, there are still many challenges in how AI can better connect to Agents when facing the physical world. The data in the physical world is often complex, diverse, and dynamically changing. Relying solely on traditional MCP protocol calling tools is difficult to meet the needs of achieving accurate and real-time interaction in complex physical environments.

Global technology companies are investing more in the digitization of the physical world

Through Google Earth and Street View projects, Google transforms geographic elements and buildings of the physical world into three-dimensional digital models, becoming an important source of data in fields such as AI, autonomous driving, logistics, and urban planning.

NVIDIA and SoftBank are jointly promoting AI-RAN (radio access network) solutions to help Japan build a strong AI infrastructure and become a global leader in AI technology.

Tesla proposed the concept of "world model" to use artificial intelligence to build and understand a high-precision simulation system of the real world. It can generate a comprehensive understanding of the physical environment and predict future scenarios, thereby achieving deep interaction with the real world and smarter decision-making.

SpaceX's Starlink provides high-speed Internet access through thousands of low-orbit satellites covering the world, incorporating the physical space of human activities into the Internet digital network.

In this context, the importance of AI networks has become increasingly prominent. AI networks aim to build a bridge between the physical world and the digital world. Through high-precision sensors and IoT technology, they can achieve real-time perception and data collection of the physical world. With the help of high-speed communication networks, they can ensure low-latency data transmission. With powerful cloud computing and edge computing capabilities, they can quickly process and intelligently analyze massive amounts of data to provide decision support for AI Agents.

AI Network Applications in Autonomous Driving

The field of autonomous driving is a typical example of AI networks helping intelligent entities interact with the physical world in real time. The MogoMind big model can be used to map the physical world into digital twins in real time, improving road traffic efficiency and driving safety. Specifically, its technical architecture is as follows:

Multimodal fusion perception layer : The perception matrix deployed on the roadside achieves 400 meters of coverage without blind spots, integrates solid-state laser radar, millimeter-wave radar, and high-definition panoramic cameras to build a three-dimensional dynamic map with centimeter-level accuracy. Through the time-space calibration algorithm, the multi-sensor data fusion error is controlled at the centimeter level, realizing accurate modeling of complex scenes.

Cognitive reasoning engine layer : Based on a deep neural network trained with real road condition data, MogoMind has the ability to predict risks beyond that of human drivers. When non-motor vehicles gather at intersections, the system will automatically generate a "high probability of crossing" warning and simulate the optimal obstacle avoidance path through the digital twin model; in the face of sudden accidents in tunnels, vehicle warnings can be triggered in advance.

Real-time decision distribution layer : Relying on the self-developed edge cloud collaborative architecture, MogoMind controls the key decision latency within 100ms. Through C-V2X and 5G-A dual-mode communication, the system can simultaneously push differentiated control instructions to vehicles within a range of 500 meters, plan the optimal passage trajectory for self-driving buses, send blind spot warnings to social vehicles, and dynamically adjust the phase of intersection lights.

In the future, AI smart traffic management systems will conduct spatiotemporal fusion analysis through real-time analysis of road network video streams, multi-source IoT sensor data, and meteorological information, and promote the evolution of traffic management mode from " delayed response" to "real-time perception".

The future of MCP: ecological competition and integration

The development of MCP is still in its early stages, and the competition among major manufacturers is mainly focused on ecosystem construction. Each manufacturer is working hard to build its own MCP ecosystem to attract more developers and users. However, due to differences in the MCP implementation details of each manufacturer, the ecosystem is fragmented. In the future, as the standardization process of MCP advances and the industry's demand for interconnection and interoperability increases, major manufacturers may achieve integration and synergy of the MCP ecosystem to a certain extent.

At the same time, with the continuous advancement of AI technology and the expansion of application scenarios, MCP will continue to evolve and upgrade. It will be combined with more new technologies, such as quantum computing and blockchain, to bring more powerful capabilities and broader development space to AI applications.

MCP is reshaping the way AI applications are developed and used. It builds a bridge between AI models and the outside world, allowing intelligence to flow in various scenarios. In this process, the active participation and layout of major companies such as Baidu, Alibaba, Tencent, and ByteDance have not only promoted the development of MCP technology, but also brought new opportunities and challenges to the entire technology industry. In the future, with the continuous improvement of the MCP ecosystem and the development of AI networks, AI will be more deeply integrated into our lives and work, creating greater value for mankind.