Communication Framework Launch

MCP and AI Agent: A New Framework for Artificial Intelligence Applications 1. Introduction to the concept of MCP In the field of artificial intelligence, traditional chatbots often lack personalized settings, resulting in monotonous responses. To address this issue, developers introduced the concept of "character setting," endowing AI with specific roles and personalities. However, even so, AI remains a passive responder, unable to proactively execute complex tasks. To break through this limitation, the Auto-GPT project has emerged. It allows developers to define tools and functions for AI, enabling the AI to automatically perform tasks according to preset rules. Although Auto-GPT has achieved a certain degree of autonomous execution, it still faces issues such as inconsistent tool calling formats and poor cross-platform compatibility. In response to these challenges, MCP (Model Context Protocol) was born. MCP aims to simplify the interaction between AI and external tools through a unified communication standard.

MCP (Model Context Protocol) enhances the interaction efficiency between AI and external tools through a unified standardized interface, promoting the self-execution capabilities of AI Agents in Blockchain automation and smart contracts execution. Although current projects are mostly in the validation stage, the MCP protocol demonstrates broad application potential.

The Integration of MCP and AI Agent: A New Paradigm for Artificial Intelligence Applications The field of artificial intelligence has long faced a challenge: how to give AI stronger personalization and execution capabilities. Traditional chatbots often lack distinct character traits, responding in a single manner and lacking warmth. To address this issue, developers have introduced the concept of "character setting", assigning specific roles and tones to the AI. However, even with rich "character settings", AI remains a passive responder, unable to proactively execute complex tasks. To break through this limitation, Auto-GPT has emerged. It allows developers to define a series of tools and functions for the AI, enabling it to automatically execute tasks and return results based on preset rules. This innovation transforms AI from a passive conversationalist into an active task performer. However, Auto-GPT still faces issues such as inconsistent tool call formats and poor cross-platform compatibility. To cope with

The MCP protocol enhances the interoperability of AI Agents with external tools through a unified communication standard, enabling them to proactively execute complex tasks. Combined with blockchain technology, MCP shows great potential in areas such as Decentralized Finance. However, the project still needs to address challenges such as product development latency and market trust.

The MCP protocol simplifies the interaction between AI and external tools by providing a unified communication standard, enhancing the proactive task execution capabilities of AI Agents. Combined with blockchain applications, MCP brings broad prospects to fields such as Decentralized Finance, but currently faces challenges such as technical integration and market trust.