What is Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard developed by @AnthropicAI that revolutionizes how AI models connect with external data sources and tools. Initially, the standard’s impact was slow, but since @OpenAI adopted it early this year it has rocketed in adoption. It is often likened to a “USB-C port for AI agents” - it provides a uniform method for connecting them to various tools and data sources, simplifying how AI interacts with external resources.

Instead of developers creating custom integrations for every data source or tool, MCP establishes a standardized communication protocol between AI models (clients) and data/tool providers (servers). The goal is to help frontier models produce better, more relevant responses by connecting them to systems where data lives, including content repositories, business tools, and development environments.

At its core, MCP addresses the fundamental limitation of Large Language Models (LLMs) being isolated from real-time data and unable to take direct actions externally. MCP enables AI systems to discover and interact with available tools dynamically, supporting persistent two-way communication between models and external systems. This is especially important and powerful in enabling autonomous AI agents to have more robust capabilities, particularly in DeFi.

How MCP Optimizes AI Agents in DeFi

MCP greatly increases AI agent capabilities in DeFi by streamlining how agents process and interact with real-time data. MCP allows AI agents to dynamically access external data streams, such as market data, from sources like relational databases and APIs. This makes it easier for agents to ingest the latest developments, and improves their ability to make informed decisions. By integrating various data sources in real-time, agents can analyze complex data points and adapt to changing market conditions – a critical task for use cases like liquidity provisioning.

MCP also enhances the efficiency of AI agents by enabling tools to take actions. Agents can not only pull data from external systems but also push updates or actions back into those systems, like executing smart contracts or updating liquidity positions. This empowers agents to autonomously execute DeFi strategies, making them more efficient actors in the space. By removing the need for custom integrations for each tool or data source, MCP reduces complexity and accelerates the deployment of AI-driven DeFi solutions. This enables agents to quickly adapt, scale, and respond to new opportunities, increasing the overall efficiency of DeFi operations.

MCP is great for providing these core capabilities for agents - tools to obtain data and to take actions. But, by contrast, it’s not well suited for agents to coordinate or communicate with each other. Unlike tools, agents aren’t designed to follow rigid commands via a fixed API. They are naturally flexible, using natural language to execute a range of capabilities and orchestrate interactions that often involve shared states. I elaborate on this in the section “MCP Accelerates the Need for Agent Swarm Coordination” below.

For those that are new to the concept, leading industry voice @S4mmyEth has written a detailed article on MCP labelling it “a major unlock for crypto and open source AI” – you can find that piece below.

MCP Adoption Across Industries

Web3 and Blockchain

Web3 is a natural hotbed of innovation, and is quickly becoming a testing ground for AI systems and methodologies. This is the same with MCP, which is enhancing AI-blockchain integration and paving the way for intelligent systems to efficiently interact with decentralized applications, unlocking new efficiencies in Web3, as noted recently by @aelfblockchain.

There are several exciting projects in the Web3 ecosystem that are leaning into MCP, they include:

@Arcdotfun – the leading Rust framework for AI agents in Web3 has just announced Ryzome, a universal app store for agentic AI, powered by MCP, which standardizes communication between AI agents and digital services. This allows AI agents to easily access Web 2 and Web 3 services without complex integrations.

@heurist_ai – a decentralized AI-as-a-Service Cloud, has released a number of tools that are accessible by MCP like an integration for @getmasafi X data.

Check their Github for an in depth look at their agent framework.

@UnifaiNetwork – a Web3 AI startup has positioned themselves as MCP for Web3, building a rich array of open source MCP plugins with capabilities including, wallet payments, swaps, liquidity management strategies, AI powered betting, and more.

@StoryProtocol – the World’s IP Blockchain, has also recently announced integrations with MCP to make it easy for AI agents to get information about transactions, licenses, transactions and ownership in their ecosystem, as well as allowing agents to create and transfer IP.

These implementations by innovative teams in the space are enabling LLMs to efficiently interact with blockchain data in real-time, perform security audits on smart contracts, track token metrics, and even facilitate on-chain transactions with proper safeguards.

E-commerce and Retail

In the e-commerce and retail space, MCP is transforming how AI agents connect with data sources and tools, improving both operational efficiency and customer experiences. Functions like product searches, order tracking, and price recommendations are streamlining operations and enhancing the overall shopping experience.

Early MCP integrations in the space include:

@Shopify stores are integrating MCP to easily manage products, customers, orders, and more with simple API calls to their Admin API.

@blocks one of the leading payment providers has used MCP to build an open source extensible AI agent, named Goose, that helps install, execute, edit, and test code with any LLM.

@WooCommerce has included MCP servers to improve interaction with their stores, enabling comprehensive tools for managing products, orders, customers, shipping, taxes, discounts, and store configuration.

Enterprise B2B

In the enterprise sector there has been significant MCP adoption for business operations and workflows. MCP has experienced adoption across leading enterprise offerings including:

@OpenAI has integrated the technology to improve the standardized communication between AI agents and external systems, streamlining enterprise workflows and reducing development overhead.

@Microsoft has included MCP in products like Copilot Studio and Semantic Kernel, enabling makers to connect directly to existing knowledge servers and APIs. Actions and knowledge are automatically added to the agent—and continuously updated as functionality evolves.

@Databricks has adopted an MCP server that connects to their API, allowing LLMs to run SQL queries, list jobs, and get updated job status’.

Development and Engineering Tools

Software development has been one of the earliest and most robust adopters of MCP. As NSHipster points out in a recent article, “Language Server Protocol (LSP) revolutionized how programming languages integrate with developer tools. Model Context Protocol (MCP) aims to do the same for a new generation of AI tools.”

Some major development and engineering tools that are now supporting the integration of MCP include:

@zeddotdev @Replit @codeiumdev and @Sourcegraph are working with MCP to enhance their platforms, by enabling agents to better retrieve information to understand the context around a coding task, producing more nuanced and functional code.

@github MCP servers provide seamless integration with their APIs, enabling advanced automation and interaction capabilities for developers and tools.

IDE integrations for code analysis and generation, transforming AI from a passive assistant to an active, collaborative partner in the software development process.

The Expanding Reach and Impact of MCP for AI Systems

The rapid adoption of MCP across industries highlights its value as a standardized protocol for AI-tool interaction. Originally an Anthropic initiative, it has now evolved into an open ecosystem with thousands of community-built servers and integrations from major technology companies. Recently, we’ve seen tremendous growth in the accessibility of MCP servers, with over 300 available specifically for AI agents, as shown by @Sumanth_077 below.

As MCP matures, we’re seeing:

1. Simplified integrations - replacing custom connectors with standardized interfaces.
2. Enhanced security through protocol-level authentication and access control.
3. A thriving developer ecosystem creating specialized tools and connectors.
4. Cross-platform compatibility between different AI models and applications.

MCP Accelerates the Need for Agent Swarm Coordination

While MCP solves the connectivity problem between individual AI agents and data sources, it doesn’t address the coordination challenge among multiple specialized agents. This is where @TheoriqAI comes in.

Theoriq has been championing the use of agent swarms (which we referred to as collectives before the term swarms took off) for the past two years. As we develop the Theoriq Protocol, a decentralized, multi-agent protocol for AI-driven finance, we are laying the foundation for agents to communicate, collaborate and execute on complex financial tasks. We are already tackling this challenge head on, by building an Onchain Liquidity Provisioning (OLP) swarm built on the protocol that delivers financial value to the DeFi ecosystem and its players. More on that below.

Specialized agents will continue to emerge and become efficient at the tasks they are built for, and as each leverage MCP for data access, they will still require “rails to communicate” with each other. Adding numerous MCP plugins to a generic agent will be less effective than having specialized agents that communicate through a coordinated protocol.

The addition of MCP makes it easier for agents to connect to external sources, and adding this capability to what Theoriq are working on, will only increase agents capabilities.

The Theoriq protocol addresses this next-level challenge by:

1. Enabling agent-to-agent communication – While MCP connects agents to data sources, Theoriq connects agents to other agents in a trust-minimized way, including supportive and long-lived communications. Importantly MCP only allows an agent to initiate requests for information, but Theoriq allows agents to be called back whenever an important event occurs (like a change in the market or breaking news).
2. Providing coordination mechanisms – Theoriq’s swarms allow specialized agents to work together on complex tasks like liquidity provisioning - they can communicate in natural language with rich semantics whereas MCP follows a traditional API paradigm with more rigid, narrowly defined capabilities.
3. Establishing economic incentives – Unlike MCP, Theoriq utilizes token economics and payment to incentivize high-quality agent contributions and participation with financial outcomes.
4. Securing agent interactions – Theoriq’s on-chain/off-chain architecture ensures secure, verifiable agent communication beyond what MCP offers.
5. Supporting agent discovery and reputation - Theoriq enables agents to discover each other based on capabilities and their track records, promoting more effective collaboration.

MCP as the Agent Tooling Layer, Theoriq as the Coordination Layer

Model Context Protocol has emerged as essential infrastructure connecting AI models to data and tools. It standardizes how agents interact with the external world, making specialized, capable agents increasingly feasible and valuable.

However, as these specialized agents proliferate, the need for coordination among them grows. Theoriq fills this critical gap by providing the “rails” for agent-to-agent communication, enabling complex multi-agent systems to tackle sophisticated challenges like on-chain liquidity provisioning.

The combination of MCP for agent-world connectivity and Theoriq for agent-agent coordination creates a powerful foundation for the emerging agentic economy. This synergy allows for specialized excellence rather than generalized mediocrity, pointing the way toward a more efficient, capable, and trust-minimized AI ecosystem. We anticipate that all the leading AI agent frameworks in Web3 will embrace MCP, just as Rig has done. As we collaborate with these frameworks to integrate Theoriq for swarm coordination, we expect both MCP and Theoriq to increase in value.

Disclaimer:

1. This article is reprinted from [Ron Bodkin]. All copyrights belong to the original author [Ron Bodkin]. 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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