Bridging AI and External Tools: An Exploration of MCP Technology

The significance of artificial intelligence lies in liberating human labor and improving work efficiency. However, current large language models still have limitations, requiring multiple rounds of dialogue to provide suggestions, and users still need to personally execute these suggestions. This falls somewhat short of the vision of truly leveraging AI to assist in work.

If we can utilize AI to engage in conversations, actually use computers for tasks such as email replies and report writing, or even automate trading, this will bring us closer to the goal of liberating productivity. This technology is currently a hot topic in the field of AI - MCP.

What is MCP?

MCP (Model Context Protocol) is a standardized protocol set to be released in November 2024, aimed at addressing the issue where AI models can only "speak" but cannot "act." The name MCP can be broken down into:

• Model: Refers to various large AI language models
• Context: Additional data or external tools provided to the model
• Protocol: A universal, standardized specification or interface

In short, MCP enables AI to not only engage in conversation but also directly control external tools to complete various tasks through standardized protocols.

Traditional large language models like ChatGPT and Grok can only perform "text input and text output" interactions. To enable AI to carry out actual operations, such as reading files, sending emails, querying databases, etc., users typically need to manually act based on AI's suggestions and then provide the results back to the AI, creating a repetitive cycle.

The emergence of MCP allows AI to directly read local files, connect to remote databases, and even operate specific network services. This means that AI is no longer limited to text output, but can replace humans in completing many repetitive or procedural tasks.

How MCP Works

The operation of MCP involves the following key components:

1. MCP Host (Administrator): Responsible for managing and coordinating the operation of the entire MCP. For example, Claude Desktop is a type of Host that can assist AI in accessing local data or tools.

2. MCP Client: Receives user requests and communicates with the AI model. Common examples include chat interfaces or IDEs integrated with MCP.

3. MCP Server: It can be seen as a set of annotated APIs that provide functionalities usable by AI, such as reading databases, sending emails, managing files, calling external services, etc.

With MCP, AI can not only understand human language but also directly convert specific text into action instructions, thus completing automated operations. For example, organizing sales reports, sending customer emails, and even operating in 3D modeling software.

Importance of MC

1. Bridging AI with external tools

The limitation of large language models lies in the fact that their data has been pre-trained and is not updated in real-time. MCP allows AI to access and manipulate external resources in real-time, greatly expanding the boundaries of AI's capabilities.

2. Standardization and Universality

MCP provides a unified standard for interaction between AI and external tools, similar to the role of a USB-C interface. This avoids the problem of redundant development and improves development efficiency.

3. From passive response to active execution

Traditional AI tools can only answer questions, while MCP enables AI to decide what instructions to execute based on actual conditions and adjust subsequent actions based on feedback results.

4. Security and Control

MCP does not require transferring all data to the AI model; data access can be controlled through permissions and API key management to ensure the security of sensitive information.

Comparison of MCP and AI Agent

AI Agent usually refers to AI systems that can automate specific tasks, capable not only of conversing but also of proactively taking actions, calling tools or APIs to complete a series of steps based on context.

The main differences between MCP and AI Agent:

• MCP is a protocol, while AI Agent is a concept or execution method.
• MCP focuses on how to enable different AI models to communicate with external tools, playing the role of a universal standard.
• The AI Agent emphasizes that AI has the ability to take proactive actions and execute tools.

MCP can help AI Agents operate more effectively, allowing them to access various external resources simply by adhering to MCP specifications, without the need to write separate API rules for each tool or platform.

MCP Concept Projects in the Cryptocurrency Field

1. Basic MCP

The framework developed by Base allows AI applications to interact with the Base blockchain. Users can deploy contracts or use DeFi services through natural language conversations.

2. Flock

Decentralized AI training platform that provides Web3 agent models, enabling AI-driven blockchain tasks to run locally, giving users more control.

3. LYRAOS

The multi-AI Agent operating system allows AI Agents to interact directly with the Solana blockchain to perform cryptocurrency transactions and other operations. Exploring the establishment of AI-driven decentralized autonomous organizations using MCP-OS.

Conclusion: A New Chapter in AI Narratives

Although MCP provides standardized rules for the interaction of AI with external tools, successful cases in the Web3 field are still limited. This may be due to several reasons:

1. The technical integration is not yet mature: There is a significant difference between various chains and DApps in the Web3 ecosystem, and unifying them into an MCP Server requires a substantial amount of development resources.

2. Security and Regulatory Risks: Allowing AI to directly operate contracts and handle financial transactions requires a comprehensive private key management and access control mechanism.

3. User Experience and Habits: Most users still have doubts about AI managing wallets or making investment decisions, and the high barriers to blockchain operations may affect adoption rates.

4. Market Sentiment: The previous hype generated by AI Agents in the cryptocurrency market is facing a cooling down, and investors are more cautious towards purely conceptual projects.

The combination of MCP and blockchain indeed has potential, but it also faces dual challenges of technology and market. If in the future it can integrate more mature security mechanisms, provide a more intuitive user experience, and develop truly valuable innovative applications, "Web3 + MCP" may surpass speculation and become the main player in the next round of technological innovation.