MCP may become a catalyst for the next phase of development in agentic crypto products.
Author: s4mmy
Translation: Deep Tide TechFlow
If you are curious like me, you might be wondering: "What exactly is MCP?!" … Why are so many people talking about it?
Information about MCP is quite limited, which is not surprising—after all, it was only born four months ago. Therefore, I decided to delve deeper and compile my findings here.
Brief Summary (TLDR): MCP is a significant breakthrough in the crypto space and open-source AI. You need to pay attention to it because it may become a catalyst for the next phase of development in agentic crypto products.
Table of Contents
Introduction
What is the Model Context Protocol (MCP)?
How MCP Serves AI Agents
The Agentic Future: Why MCP is Crucial
Other Projects Similar to MCP
Key Differences with Traditional AI Integration
Conclusion
1) Introduction
As AI agents continue to evolve, becoming more autonomous and gradually integrating into real-world applications, the Model Context Protocol (MCP) is revolutionizing the way these agents interact with external data and tools.
MCP was launched by Anthropic at the end of 2024 and aims to become a standardized framework that empowers AI agents to achieve seamless communication with diverse data sources.
Since @anthropicai introduced this communication standard, an increasing number of AI solutions have adopted it as the new industry default.
In simple terms, MCP is the way “AI communicates with software in real-time.”
With the arrival of the agentic future—a time when AI systems can independently solve complex tasks—will MCP become the key to unlocking the next wave of AI innovation?
Perhaps it will also drive the prices in the field of Crypto and AI integration to new heights?
From chatbots to autonomous systems driving various industries, AI agents are expected to make real-time decisions and gather dynamic information from multiple data sources. However, a major bottleneck has always existed: the lack of a standardized way for AI models to connect to external systems, such as databases, file repositories, or business tools.
This is where MCP comes into play.
The Model Context Protocol (MCP) serves as an open standard designed to fill this gap, enabling AI agents to dynamically access and interact with external data sources.
It allows large language models (LLMs) to effectively act as agents, even capable of deploying smart contracts or executing DeFi (decentralized finance) operations. This is undoubtedly a huge breakthrough!
If you, as a user in the crypto space, have ever used ChatGPT, you may have realized that it performs quite poorly in providing timely crypto insights or specific information analysis—if it can't even tell me the current spot price of the top 100 cryptocurrencies, I wouldn't be surprised!
MCP opens up possibilities for enhancing AI-driven DeFi capabilities, such as:
“Find the best annual percentage yield (APY) for USDC and allocate $1,000”;
Rebalance a portfolio based on market fluctuations.
This marks a broader trend towards the agentic future, where AI systems will operate with greater independence and practicality. This development also significantly distinguishes itself from traditional AI systems, perfectly aligning with the permissionless nature of the crypto space.
2) What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP), launched by Anthropic at the end of 2024, is an open-source standard designed to connect AI assistants.
Especially AI agents powered by large language models (LLMs) interact with external systems to obtain those coveted real-time data.
MCP can be seen as a universal adapter that allows AI agents to access:
Content repositories
Business tools
Development environments, and more!
Why Should You Pay Attention to It?
Unlike traditional AI integration methods (which often rely on decentralized, customized solutions), MCP provides a unified bidirectional communication framework. This means AI agents can not only extract data from external systems but also push updates or actions back to those systems, enabling more dynamic and autonomous behavior. For example, you could have a fully autonomous agent that can update enterprise systems or manage your personal affairs!
Anthropic's mission in launching MCP is to simplify the integration process of AI, making it easier for developers to build agentic workflows that allow AI systems to operate independently and contextually.
3) How MCP Serves AI Agents
MCP acts as an integration layer, allowing AI agents to connect to external services on demand. Here’s how it works:
a) Dynamic Data Access
Unlike traditional methods that rely solely on pre-trained data, AI agents using MCP can access real-time or context-specific data from sources such as relational databases, file systems, or code repositories. For example, those hard-to-get real-time cryptocurrency prices can also be achieved through MCP! Even @0rxbt is trying to use MCP for our favorite "Purple Frog", also known as SkyNet, or @aixbt_agent.
b) Bidirectional Communication
MCP supports bidirectional interaction, meaning AI agents can not only retrieve data but also take actions based on their analysis, such as updating databases or triggering workflows.
c) Standardized Framework
By providing a universal protocol, MCP eliminates the need for customized integrations, reducing development complexity and ensuring consistency across applications. This may be the answer to the fragmentation issues of various programming languages and protocols in the blockchain ecosystem! Perhaps AI agents will become the aggregation layer across chains and multiple languages!
4) The Agentic Future: Why MCP is Crucial
AI agents are no longer just passive systems; they are becoming entities capable of proactive decision-making and goal-oriented actions, with the ability to autonomously complete tasks.
However, to make AI agents truly useful, they need to break through the limitations of training data and interact smoothly with the real world, which is precisely where MCP comes into play.
An excellent example of MCP's practical application comes from Anthropic's official documentation:
Imagine an AI agent tasked with managing a software development pipeline.
Through MCP, this agent can:
Extract the latest code from the code repository;
Analyze vulnerabilities in the code;
Then push reports back to the team's project management tool in real-time.
The following (credit to @alexalbert__) is a case: we can see Anthropic's Claude directly connected to GitHub, creating a new code repository, and submitting a PR (Pull Request) through MCP integration.
MCP enables AI agents to adapt to changing contexts by accessing real-time data, making them more flexible and intelligent.
The following shows the integration and communication capabilities of MCP with various platforms such as GitHub, Web APIs, Slack, email, and more.
MCP provides answers to @davidsacks regarding the potential characteristics of “The Winning Agent”:
Perhaps connecting agents to real-world infrastructure is the true winning formula!
By standardizing protocols, developers no longer need to "reinvent the wheel" for each new integration, allowing them to build agentic workflows more quickly.
The core of the agentic future lies in AI systems being able to act independently to achieve complex goals, whether it be:
Automating business processes,
Managing supply chains,
Or even assisting in scientific research.
MCP becomes an important step towards achieving this vision by providing the infrastructure for AI agents to interact with the real world.
5) Other Initiatives Similar to MCP
Anthropic is not the only company recognizing the need for standardized AI integration protocols. Recently, several major protocols and companies have launched or adopted frameworks similar to MCP to support the agentic future:
i) Perplexity MCP
ii) OpenAI Agents SDK MCP Just yesterday, OpenAI released the MCP plugin for its Agents SDK:
iii) Stripe MCP Integration
…Additionally, more and more MCP servers are being developed to facilitate smoother AI communication:
Some CEOs independent of Anthropic have also begun to recognize the importance of MCP in advancing the development of AI agents.
These initiatives highlight a growing trend: to achieve agentic AI, standardized and scalable data integration solutions must be adopted.
While MCP maintains its lead due to its open-source nature and broad applicability, the involvement of major players like xAI, Google, and Meta further underscores the importance of this field.
6) Key Differences with Traditional AI Integration
Why does MCP (and similar frameworks) have advantages over traditional AI integration methods?
Traditional integration often relies on custom APIs or middleware, leading to fragmented and hard-to-scale solutions.
MCP provides a universal standard, reducing complexity and ensuring consistency. This can be clearly illustrated with a comparison chart:
Open Source Collaboration: The open-source nature of MCP fosters collaboration across the industry, contrasting sharply with the isolated approaches of centralized AI companies. This is a significant value proposition in the crypto space.
Here’s a quick comparison:
Some high-level examples showcase the potential applications of MCP in the crypto space:
We are already beginning to see some driving forces, such as:
DeFAI (the combination of decentralized finance and AI) solutions, such as @danielesesta's @heyanonai, @LimitusIntel, or @gizatechxyz;
Custom tools for on-chain analysis, such as @aixbt_agent.
As MCP further integrates into the broader crypto and AI ecosystem, more innovations will emerge in the future!
7) Conclusion
MCP represents an important step towards the agentic AI future. In this future, autonomous systems can seamlessly interact with the surrounding world.
By providing a standardized framework for connecting AI agents to external data sources, MCP addresses a critical bottleneck in AI development, making solutions smarter, more adaptable, and more scalable.
The industry's acceptance and support for protocols similar to MCP indicate a trend towards a collective effort towards the agentic vision.
However, challenges remain:
The success of MCP and similar frameworks will depend on widespread adoption, interoperability between protocols, and the ability to keep pace with the rapidly evolving AI environment.
As we move towards a future where AI agents play an increasingly important role in our lives, frameworks like MCP will become vital bridges connecting AI with real-world applications.
Whether MCP ultimately becomes the de facto industry standard or merely serves as a catalyst for further innovation, it has already sparked a critical discussion about the infrastructure needed for agentic AI and agentic crypto products.
Disclaimer: The content of this article is for informational and educational purposes only and should not be considered investment or financial advice. The author holds no financial interest in any of the protocols mentioned in the article.
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