Original Title: Model Context Protocol (MCP): The Next Crypto AI Catalyst
Original author: s4mmy
Source:
Compiled by: Daisy, Mars Finance
If you are like me, you might be wondering “What exactly is MCP?!”… Why are so many people talking about it?
The literature on it is quite limited, which is normal; it was only born four months ago. Therefore, I decided to conduct research and organize my findings here.
Summary: It is a significant breakthrough in cryptocurrency and open-source AI. So you need to pay attention to it; it may catalyze the next phase of agency crypto products.
Directory
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Introduction
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What is the Model Context Protocol?
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How does MCP work for AI agents?
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Agency of the Future: Why MCP is Important
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Other measures similar to MCP
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The main differences from traditional AI integration
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Conclusion
- Introduction
As AI agents continue to evolve, becoming more autonomous and integrating into real-world applications, the Model Context Protocol (“MCP”) emerges as a game-changing technology, transforming how these agents interact with external data and tools.
MCP will be launched by Anthropic at the end of 2024, positioned as a standardized framework aimed at empowering AI agents to communicate seamlessly with multiple data sources.
Since @anthropicai introduced this communication standard, more AI solutions have adopted it as the norm.
In simple terms, it is: “the way AI communicates with software in real time.”
With the advent of agents in the future—the era where AI systems independently execute complex tasks—could MCP be the key to unlocking the next wave of AI innovation?
Could it be the next wave of price increases for crypto and AI?
From chatbots to autonomous systems driving various industries, AI agents are increasingly expected to make real-time decisions and obtain real-time data from multiple sources.
However, a major bottleneck remains: the lack of a standardized way for AI models to interface with external systems, such as databases, repositories, or business tools.
This is where the role of MC lies.
Introducing the Model Context Protocol (MCP) - an open standard designed to bridge this gap by enabling AI agents to dynamically access and interact with external data sources.
It enables large language models (LLMs) to effectively act as agents, capable of deploying smart contracts or executing DeFi activities. This is a tremendous breakthrough!
If you have used ChatGPT as a crypto native user, you may have realized that it performs poorly in providing timely crypto insights, specific information, or analysis—I’d be very surprised if it could tell me the current spot prices of some of the top 100 cryptocurrencies!
MCP can enhance AI-driven DeFi capabilities, such as:
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“Find the best annual yield for USDC and allocate 1000 dollars,” or;
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Rebalance the investment portfolio based on market fluctuations.
This indicates a broader trend towards a future of agency, in which AI systems will be more independent and useful.
The difference from traditional AI systems is that traditional systems are constrained by the permissionless nature of encrypted rails.
- What is the Model Context Protocol (MCP)?
The Model Context Protocol (“MCP”) was launched by Anthropic at the end of 2024 and is an open-source standard designed to integrate AI assistants,
Especially AI agents powered by large language models (LLMs) that connect to external systems containing rich real-time data.
It can be seen as a universal adapter that allows AI agents to access safely and in a standardized way:
Content Library
Business tools
Development environment, and there’s more!
Why should you care?
Unlike traditional AI integration, which typically relies on fragmented, customized solutions, MCP provides a unified framework for bidirectional communication.
This means that AI agents can not only retrieve data from external sources but also push updates or operations back to these systems, enabling more dynamic and autonomous behavior.
You can allow an agent to autonomously update business systems or manage your personal affairs!
Anthropic’s mission with MCP is to simplify AI integration, helping developers build agent workflows more easily, allowing AI systems to operate independently and contextually.
- How does MCP work for AI agents
MCP serves as an integration layer that allows AI agents to connect to external services based on demand. Here is a detailed explanation of how it works:
a) Dynamic Data Access:
AI agents using MCP can access real-time or context-specific data, rather than just relying on pre-trained data. They can obtain data from sources such as relational databases, file systems, or code repositories.
The mysterious cryptocurrency prices can be obtained in real time! Even @0rxbt is playing with our favorite purple frog (that is, SkyNet, also known as @aixbt_agent) using MCP!
b) Bi-directional Communication:
MCP supports bidirectional interaction, which means that the AI agent can not only retrieve data but also perform actions based on analysis — for example, updating databases or triggering workflows.
c) Standardization Framework:
By providing a universal protocol, MCP eliminates the need for custom integrations, reduces complexity for developers, and ensures consistency across various applications.
Maybe this is the solution to the problems of different blockchains and multiple programming languages! Perhaps the proxy will become the aggregation layer?!
- Agent Future: Why MCP is Important
AI agents are no longer just passive systems; they are becoming active, goal-oriented entities capable of making decisions autonomously.
However, in order for AI agents to be truly useful, they need to break through the limitations of training data and be able to interact smoothly with the real world.
This is exactly the role of MC.
A good example of an MCP application comes from Anthropic’s documentation:
Assume an AI agent is responsible for managing the software development pipeline.
Through MC, agents can:
Pull the latest code from the code repository.
Analyze the bug in the code
Then the report is pushed back to the team’s project management tool—everything is done in real time.
The following (thanks to @alexalbert__) demonstrates Anthropic’s Claude directly connecting to GitHub, creating a new repository and initiating a PR (pull request) 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 shows the integration and communication of MCP with GitHub, Web API, Slack, email, and more.
MCP provides a solution for @davidsacks’s statement on what a “winning” agent might look like:
But perhaps the infrastructure that connects agents with the real world is the key to victory!
By using standardized protocols, developers can build proxy workflows faster without reinventing the wheel for each new integration.
The core of the agency of the future is that the AI system can act independently to achieve complex goals—whether it is:
Automated business processes
Manage Supply Chain
even assist scientific research
MCP is an important step towards achieving this vision, providing the infrastructure for AI agents to interact meaningfully with the world.
- Other measures similar to MCP
Anthropic is not the only participant recognizing the need for standardized AI integration protocols.
Recently, several large protocols and companies have launched or adopted frameworks similar to MCP to support the development of agents in the future:
i) Perplexity MC:
ii) OpenAI Agents SDK MC:
Recently (actually yesterday), OpenAI released its MCP plugin for the Agents SDK:
iii) Stripe MC integration:
…there are many MCP servers in development to make AI communication more seamless:
CEOs different from Anthropic are also acknowledging its importance in advancing the future of AI agents:
These measures highlight a growing trend: recognizing that proxy AI requires standardized, scalable data integration solutions.
Although MCP remains a leader due to its open-source nature and wide applicability, the entry of significant players like xAI, Google, and Meta further emphasizes the importance of this field.
- Key differences from traditional AI integration
Why does MCP (and similar technologies) have advantages over traditional AI integration methods?
Traditional integration often involves custom APIs or middleware, leading to fragmented solutions that are difficult to scale.
MCP provides a universal standard, reducing complexity and ensuring consistency. This comparison chart clearly illustrates the differences.
Open Source Collaboration: The open-source nature of MC promotes collaboration across the entire industry, which stands in stark contrast to the closed approach of centralized AI companies.
This is a significant value proposition for cryptocurrency.
Here is a quick comparison:
Here are some high-level application examples in the cryptocurrency field:
We are starting to see the momentum on (1) DeFAI solutions, such as @danielesesta’s @heyanonai, @LimitusIntel, or @gizatechxyz, as well as addressing on-chain analysis issues through customized tools like @aixbt_agent.
With the further integration of MCP into the broader cryptocurrency and AI ecosystem, more applications are expected to emerge!
- Conclusion
MCP represents an important step towards the future of agent AI, where autonomous systems can seamlessly interact with the surrounding world.
By providing a standardized framework that connects AI agents with external data sources, MCP addresses key bottlenecks in AI development, promoting smarter, adaptable, and scalable solutions.
The broader industry’s adoption of similar MCP protocols marks a collective push towards this agency vision.
However, challenges still exist.
The success of MCP and similar technologies will depend on widespread adoption, interoperability among 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 the bridge connecting AI with real-world applications.
Whether MCP becomes a de facto standard or merely serves as a catalyst for further innovation, it has sparked critical discussions about the infrastructure required for agent AI and agent cryptocurrency products.
