Communication Framework

MCP and AI Agent: A New Paradigm for Artificial Intelligence Applications Introduction to the MCP Concept Traditional chatbots in the field of artificial intelligence often lack personalized settings, resulting in uniform responses that lack human warmth. To address this issue, developers introduced the concept of "persona", giving AI specific roles, personalities, and tones. However, even so, AI remains a passive responder and cannot actively execute complex tasks. The Auto-GPT project has emerged, allowing developers to define tools and functions for AI and register them in the system. After a user makes a request, Auto-GPT can generate operational instructions based on preset rules and tools, automatically perform tasks, and return results, transforming AI from a passive responder into an active task executor. Although Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as inconsistent tool calling formats and poor cross-platform compatibility.

The concept of MCP and its application in AI Agents Introduction to MCP Concept Traditional chatbots in the field of artificial intelligence often rely on general dialogue models, lacking personalized character settings, which results in responses that are monotonous and lack warmth. To address this issue, developers have introduced the concept of "character settings", endowing AI with specific roles, personalities, and tones, making its responses more aligned with user expectations. However, even with rich "character settings", AI remains merely a passive responder and cannot proactively perform tasks or carry out complex operations. To address this issue, the open-source project Auto-GPT has emerged. It allows developers to define a series of tools and functions for AI and register these tools within the system. When a user makes a request, Auto-GPT generates operational instructions based on preset rules and tools, automatically executing tasks and returning results, transforming AI from a passive conversationalist into an active task executor. Despite Au

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MCP and AI Agent enhance the interaction efficiency between AI and external tools by providing a unified communication standard, transforming AI from a passive responder into an active task executor. The MCP protocol has broad application potential, especially in fields like Decentralized Finance and DAO, but currently faces challenges such as development progress and market trust.

MCP and AI Agent: A New Paradigm for Artificial Intelligence Applications 1. MCP Concept Analysis Traditional chatbots often lack personalized settings, resulting in monotonous and uninteresting responses. To address this issue, developers have introduced the concept of "character setting," giving AI specific roles and tones. However, even with rich "character settings," AI remains merely a passive responder and cannot actively perform complex tasks. To overcome this limitation, the Auto-GPT project has emerged. It allows developers to define tools and functions for AI and register them within the system. After a user makes a request, Auto-GPT can automatically generate operating instructions and execute tasks based on preset rules and tools. This transforms AI from a passive conversationalist into an active task executor. Despite the fact that Auto-GPT has achieved autonomous execution of AI, it still faces issues such as inconsistent tool invocation formats and poor cross-platform compatibility. To address this,