Forward the Original Title ‘Model Context Protocol (MCP): The Next Crypto AI Catalyst’

If you’re like me you’ve probably been wondering “WTF is MCP?!” … and why are so many people talking about it?

There’s limited literature on it, and rightly so; it was only birthed four months ago. So I decided to research and consolidate my findings here.

TLDR: It’s a major unlock for crypto and open-source AI. So you need to pay attention; it could catalyze the next leg up in agentic crypto products.

Contents

1. Introduction
2. What is the Model Context Protocol?
3. How MCP Works for AI Agents
4. The Agentic Future: Why MCP Matters
5. Other MCP-Like Initiatives
6. Key Differentiators from Traditional AI Integrations
7. Conclusion

1) Introduction

As AI agents evolve to become more autonomous and integrated into real-world applications, the Model Context Protocol (“MCP”) has emerged as a game-changer in how these agents interact with external data and tools.

Launched by Anthropic in late 2024, MCP is positioning itself as a standardized framework to empower AI agents, enabling seamless communication with diverse data sources.

But since @anthropicai introduced this communication standard, more AI solutions have adopted this as the status quo

Simply put, it’s: “How AI’s talk to software” in real time

With the agentic future on the horizon—where AI systems act independently to solve complex tasks—could MCP be the key to unlocking the next wave of AI innovation?

Perhaps the next leg up in terms of Crypto x AI price action?

From chatbots to autonomous systems powering industries, AI agents are increasingly expected to make decisions in real time, drawing on live data from a variety of sources.

However, a major bottleneck has persisted: the lack of a standardized way for AI models to connect with external systems like databases, file repositories, or business tools.

Here’s where MCP fits in.

Enter 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, with the ability to deploy smart contracts or carry out DeFi activities. That’s a pretty huge unlock!

If you’ve used ChatGPT as a crypto native you’re probably aware that it’s terrible at timely crypto insights specific information or analysis—I’d be surprised if it can even tell me the current spot price on some of the top 100 cryptocurrencies!

MCPs offer the ability to enhance AI-powered DeFi, such as:

1. “Find the best APY for USDC and allocate $1K”, or;
2. Rebalancing portfolios based on market volatility.

This signals a broader movement toward an agentic future where AI systems operate with greater independence and utility.

Something that differentiates traditional AI systems from the permissionless nature of crypto rails.

2) What is the Model Context Protocol?

The Model Context Protocol (“MCP”), introduced by Anthropic in late 2024, is an open-source standard designed to connect AI assistants

Particularly AI agents powered by large language models (LLMs)—to external systems where that juicy real time data resides.

Think of it as a universal adapter that allows AI agents to tap into (In a secure and standardized way):

• Content repositories
• Business tools
• Development environments, and more!

Why should you care?

Unlike traditional AI integrations, which often rely on fragmented, custom-built solutions, MCP provides a unified framework for bidirectional communication.

This means AI agents can not only pull data from external sources but also push updates or actions back to those systems, enabling more dynamic and autonomous behavior.

You could have an agent updating business systems or managing your personal affairs fully autonomously!

Anthropic’s mission with MCP is to simplify AI integration, making it easier for developers to build agentic workflows where AI systems can operate independently and contextually.

3) How MCP Works for AI Agents

MCP acts as an integration layer that allows AI agents to connect with external services on demand. Here’s a breakdown of how it functions:

a) Dynamic Data Access:

Instead of relying solely on pre-trained data, AI agents using MCP can access real-time or context-specific data from sources like relational databases, file systems, or code repositories.

Those exotic crypto prices could be accessed in real time! Even @0rxbt is playing around with an MCP for our favourite Purple Frog, aka SkyNet, aka @aixbt_agent:

b) Bidirectional Communication:

MCP enables two-way interactions, meaning AI agents can both retrieve data and take actions—like updating a database or triggering a workflow—based on their analysis.

c) Standardized Framework:

By providing a universal protocol, MCP eliminates the need for bespoke integrations, reducing complexity for developers and ensuring consistency across applications.

Maybe this is the solution for all the different blockchains and array of programming languages! Perhaps agents will become the aggregation layer?!

4) The Agentic Future: Why MCP Matters

AI agents are no longer just reactive systems; they’re becoming proactive, goal-directed entities capable of making decisions autonomously.

However, for AI agents to actually be useful, they need to break free from the limitations of their training data and interact fluidly with the real world.

This is where MCP comes in.

A great example of MCP in action comes from Anthropic’s own documentation:

Imagine an AI agent tasked with managing a software development pipeline.

Using MCP, the agent can:

• Pull the latest code from a repository
• Analyze it for bugs, and
• then push a report back to the team’s project management tool—all in real time.

Below ( h/t @alexalbert__ ) we can see Anthropic’s Claude connecting directly to GitHub, creating a new repo and makes a PR using its MCP integration:

MCP allows AI agents to adapt to changing contexts by accessing live data, making them more responsive and intelligent.

Below demonstrates its integration and communication with GitHub, Web APIs, Slack, Emails and much more.

MCP provides a solution to @davidsacks statement about what “The Winning” Agent may look like:

But perhaps its the infra connecting agents to the real world that is the winning formula!

With a standardized protocol, developers can build agentic workflows faster, without reinventing the wheel for each new integration.

The agentic future is all about AI systems that can act independently to achieve complex goals—whether that’s:

• Automating business processes,
• Managing supply chains, or
• Even assisting in scientific research.

MCP is a critical step toward making this vision a reality by providing the infrastructure for AI agents to interact with the world in a meaningful way.

5) Other MCP-Like Initiatives

Anthropic isn’t the only player recognizing the need for standardized AI integration protocols.

Several large protocols and companies have recently launched or embraced MCP-like frameworks to support the agentic future:

i) Perplexity MCP:

ii) OpenAI Agents SDK MCP:

Most recently (yesterday in fact) OpenAI released its own MCP plugin to its Agents SDK:

iii) Stripe MCP integration:

… and many more MCP servers are being developed to make AI communication more seamless:

CEOs, separate from Anthropic, are acknowledging its importance in progressing the AI Agent future:

These initiatives highlight a growing trend: the recognition that agentic AI requires standardized, scalable solutions for data integration.

While MCP remains a leader due to its open-source nature and broad applicability, the involvement of major players like xAI, Google, and Meta underscores the importance of this space.

6) Key Differentiators from Traditional AI Integrations

Why does MCP (and its counterparts) stand out compared to traditional AI integrations?

Traditional integrations often involve custom APIs or middleware, leading to fragmented solutions that are hard to scale.

MCP offers a universal standard, reducing complexity and ensuring consistency. This comparison diagram nails it in a single image:

Open-Source Collaboration: MCP’s open-source nature fosters collaboration across the industry, contrasting with the siloed approaches of centralized AI companies.

THIS IS A MAJOR VALUE PROPOSITION FOR CRYPTO.

Here’s a quick comparison:

Here’s some high level examples of how it can be used in crypto:

We are beginning to see a push with (1) with DeFAI solutions for instance @danielesesta ‘s @heyanonai @LimitusIntel or @gizatechxyz, and on-chain analytics also being tackled with custom tooling like @aixbt_agent

Expect a lot more to come as MCP is further integrated across the broader crypto and AI ecosystems!

7) Conclusion

MCP represents a pivotal step toward an agentic AI future, where autonomous systems can seamlessly interact with the world around them.

By providing a standardized framework for connecting AI agents to external data sources, MCP addresses a critical bottleneck in AI development, enabling more intelligent, adaptable, and scalable solutions.

The broader industry’s embrace of MCP-like protocols—signals a collective push toward this agentic vision.

However, challenges remain.

The success of MCP and its counterparts will depend on widespread adoption, interoperability between protocols, and the ability to keep pace with the rapidly evolving AI landscape.

As we move toward a future where AI agents play an increasingly central role in our lives, frameworks like MCP will be the bridges that connect AI to real-world applications.

Whether MCP becomes the de facto standard or simply a catalyst for further innovation, it has already sparked a crucial conversation about the infrastructure needed for agentic AI and agentic crypto products.

Disclaimer:

1. This article is reprinted from [s4mmy]. Forward the Original Title ‘Model Context Protocol (MCP): The Next Crypto AI Catalyst’. All copyrights belong to the original author [s4mmy]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
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