Author: s4mmy, decentralized AI researcher; Translation: Golden Finance xiaozou

Are you as confused as me: What exactly is MCP? Why are so many people talking about MPC?

There is indeed very few literature for learning reference, but it makes sense - because MPC only appeared four months ago. So I decided to conduct in-depth research and compile the gains as follows through this article.

In short: MPC is a major breakthrough in the fields of encryption and open source AI. It is recommended that you keep paying attention at all times because it is likely to catalyze the next round of explosion of independent encryption products.

Table of this article

1. Introduction

2. What is the Model Context Protocol (MPC)?

3. How does MCP empower AI agents?

4. The future of autonomous: the significance of MCP

5. Other MCP-type initiatives

6. Key differences that differ from traditional AI integration

7. Conclusion

1. Introduction

As AI agents become increasingly autonomous and integrated into real-world applications, the Model Context Protocol ("MCP") is reshaping the way the agents interact with external data. The MCP standardization framework launched by Anthropic at the end of 2024 is becoming the infrastructure for AI agents to seamlessly connect to multiple data sources.

Since the advent of this communication standard, more AI solutions have adopted it as an industry norm. In short, MCP defines "the way AI talks in real time with software."

In the future where AI systems are about to solve complex tasks independently, can MCP become the key to unlock the next wave of AI innovation?

Maybe it a new engine for price trends in the AI ​​field?

From chatbots to autonomous systems driving the industry, AI agents are being placed on the high hopes of real-time decision-making, which requires them to dynamically obtain various real-time data sources.

But the long-term bottleneck lies in the lack of standardized solutions for AI models to connect external systems such as databases/file libraries/business tools—this is the breakthrough point of MCP.

Model context protocol (MCP) emerged as an open standard, enabling large language models (LLMs) to truly become agents with the ability to deploy smart contracts or perform DeFi activities, which is a major breakthrough! Encryption users have a deep understanding that ChatGPT performs poorly in real-time encrypted data analysis and may not even provide the spot price of the top 100 tokens in market value.

MCP will enhance AI-powered DeFi applications, such as finding the best annualized USDC returns and configuring $1,000, or automatically rebalancing the portfolio based on market volatility.

This marks that AI systems are evolving towards a more autonomous and practical direction, complementing the permissionless nature of encryption infrastructure.

2. What is the Model Context Protocol (MPC)?

Anthropic's MCP was launched in late 2024 as an open source standard for connecting AI assistants (especially LLM-driven agents) with real-time data sources.

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It can be regarded as a safe and standardized "universal adapter" that enables AI agents to access: content repositories/commercial tools/development environments, etc.

Its core value lies in: unlike traditional AI integration that relies on fragmented customization solutions, MCP provides a unified framework for two-way communication - an agent can not only obtain external data, but also push updates or operations to the source system to achieve true dynamic autonomous behavior.

Anthropic aims to simplify AI integration through MCP, allowing developers to build autonomous workflows with situational awareness more conveniently.

3. How does MCP empower AI agents?

MCP serves as an integration layer to enable AI agents to connect to external services on demand.

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its operating mechanisms include:

a) Dynamic data access: breaking through the limitations of pre-training data and obtaining context data from sources such as relational databases/file systems/code libraries in real time;

b) Bidirectional communication: supports two-way interaction between retrieval data and trigger operations (such as updating databases/starting workflows);

c) Standardization framework: eliminates the need for customized integration through common protocols and reduces development complexity. This may solve the compatibility problem between multi-chain and multi-programming languages, making agents a new aggregation layer.

4. The future of autonomous: The significance of MCP

AI agents are no longer just reaction systems; they are becoming proactive, goal-oriented, autonomous decision-making entities. However, for AI agents to really work, they need to get rid of the limitations of training data and interact smoothly with the real world. This is where MPC comes in.

Typical examples, as shown in the Anthropic documentation: Agents managing software development processes can pull the latest code in real time through MCP, analyze defects, and push reports to project management tools.

The following figure shows Claude directly connects GitHub to create a code base and submits a PR through MCP integration:

MCP allows AI agents to adapt to changing environments by accessing real-time data, making them more responsive and intelligent. The following figure shows its integration and communication with GitHub, Web API, Slack, mail, etc.

This is exactly the infrastructure required for DavidSacks to "winning agents".

Standardized protocols will accelerate the construction of developers' independent workflows, whether it is automating business processes, managing supply chains or assisting scientific research, MCP provides the critical infrastructure to achieve this vision.

5. Other MCP-like initiatives

In addition to Anthropic, many giants have laid out standardized AI integration protocols:

* PerplexityMCP

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* OpenAI Agent SDK's MCP plugin (Latest release)

* Stripe MCP integration

More MCP servers are under development to facilitate more seamless AI communication.

Left;">Engineering CEOs generally recognize the importance of such protocols to the development of AI agents.

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These initiatives indicate that autonomous AI requires standardized, scalable data integration solutions. Although MCP remains ahead with its open source and universality, the entry of giants such as xAI/Google/Meta confirms the key value of this field.

6. Key differences from traditional AI integration

Why does MCP (and its similar products) stand out compared to traditional AI integration? Traditional integration usually relies on custom APIs or middleware, and solutions are fragmented and difficult to scale.

MCP provides a common standard that reduces complexity and ensures consistency. This is clearly reflected in the following figure:

Open source collaboration: In sharp contrast to the isolated approach of centralized AI companies, the open source nature of MCP promotes collaboration across the industry.

This is a major value proposition of cryptocurrencies.

The following is a simple comparison chart:

The following are further examples of applications in the encryption field:

The application in the encryption field has begun to show signs: (1) DeFAI solutions such as HeyAnon/Limitus/Giza; (2) On-chain analysis tools similar to aixbt. With the deep integration of MCP in encryption and AI ecosystem, more innovations will surely emerge.

7. Conclusion

MCP marks a critical step towards the future of autonomous AI, in which autonomous systems can interact seamlessly with their surroundings.

By providing a standardized framework that connects AI agents to external data sources, MCP has solved key bottlenecks in the development of AI, giving birth to smarter, more adaptable and scalable solutions.

The entire industry's acceptance of MCP protocols is driving us to jointly move towards this vision of autonomous development.

However, the challenge remains: the success of MCP and its similar protocols will depend on the degree of widespread adoption, interoperability between protocols, and the ability to keep up with the rapid evolution of the AI ​​field.

As AI agents play an increasingly important role in our lives, the MCP-like framework will become a bridge connecting AI and real-world applications. Whether MCP eventually becomes a practical standard or serves as a catalyst for further innovation, it has sparked important discussions about the infrastructure required for autonomous AI and autonomous crypto products.