The development of MCP is a complement to buttons, Dify, etc.
The MCP protocol brings revolutionary changes to AI Agent, breaks down data silos, and improves data sovereignty and scalability.
Core content:
1. The openness and decentralization concept of the MCP protocol
2. The importance of data sovereignty and localized deployment
3. The flexibility and scalability of modular toolchain design
The Model Context Protocol (MCP) is an open standard protocol launched by Anthropic, which aims to provide a secure and standardized way for large language models to connect to external data sources, tools and services. Through a unified interface design, MCP solves the problems of data silos, complex customized integration, and strong platform dependence in traditional AI Agent development, enabling developers to quickly build flexible and secure Agent applications. There is a lot of information on its specific principles online, so I will not go into details here.
Domestic users are more familiar with AI Agent application development platforms such as Alibaba Cloud Bailian, Byte Coze, and Dify. Although these platforms can more or less meet the needs, they are generally scattered and lack unified standards. In particular, they are relatively "closed" - for example, Agents developed with Coze can only run on Coze.
MCP is designed to solve this type of problem. It has the following features:
Openness: Breaking ecological barriers The core design concept of MCP is decentralization, building standardized interfaces based on open source protocols. Tools and agents developed by developers following the MCP protocol can run seamlessly on any platform compatible with the protocol (such as Claude, third-party custom platforms, etc.). In contrast, domestic platforms such as Button and Dify adopt a closed architecture, and the developed agents can only be used within their own ecosystems, forming a technical lock-in effect. For example, agents trained on the Button platform cannot be directly migrated to other LLM service provider environments, while agents developed based on MCP can quickly access different platforms through the protocol adaptation layer.
Data sovereignty: Localization first principle MCP redefines the data security boundary through localized deployment. All MCP servers (such as file system tools and database interfaces) run in the user's local environment by default, and sensitive data can be processed without uploading to the cloud. This difference is particularly critical in highly regulated fields such as finance and healthcare - the localization characteristics of MCP naturally meet compliance requirements.
Expansion capability: Modular free combination MCP adopts Lego-style tool chain design, and developers can freely choose or customize tool modules. For example, through the open source MCP server library on GitHub, developers can directly integrate PostgreSQL database tools or Slack message interfaces, or develop private tools on their own (such as enterprise ERP system connectors). Traditional Agent development platforms usually only provide official preset tool libraries (such as limited search engines and specified knowledge bases), and custom development needs to rely on platform review, which limits flexibility. Button is currently doing a good job in this area and cooperating with Byte Traffic to build related ecosystems, but if it can further integrate MCP to create a more open platform, it will have a faster development speed.
Development freedom: Multi-model compatibility The MCP protocol itself is decoupled from the underlying model and supports the access of multiple large models such as Claude, GPT, Gemini, Qwen, Deepseek, etc. Developers can switch model engines according to scenario requirements, and even mix and call multiple models (such as using Deepseek to process creative generation and Qwen to process logical analysis).
Ecosystem collaboration: community-driven vs platform-led MCP forms a decentralized collaboration network through the open source community. Any developer can contribute tool implementation solutions and promote ecological prosperity through protocol standardization. For example, Anthropic only provides basic protocol specifications, while specific functions such as file operations and browser automation are implemented by community developers. The tool chain iteration of the traditional AI Agent platform is completely dominated by the platform, and developers can only passively use the official update function, making it difficult to participate in the underlying ecological construction.
In general, MCP achieves "infrastructure neutrality" through protocol layer standardization, and its value is similar to the HTTP protocol to the Internet - any participant who complies with the protocol can equally access the ecosystem. However, current platforms such as Kouzi are closer to "walled gardens" and rely on centralized control to maintain a closed ecological loop. This difference in openness directly determines the long-term technical autonomy and innovation space of developers.
Of course, because of the openness of MCP, Bailian, Kouzi, and Dify may also support the MCP protocol in the future to achieve compatibility and interoperability with each other, benefiting a wider range of AI Agent developers and users.
