Model Context Protocol (MCP) Analysis: Shaping the Future of AI Agents

I. The Problem Before MCP

     Before the advent of the Model Context Protocol (MCP), AI Agents, whether powered by GPT, Claude, or other Large Language Models (LLMs), operated within a closed environment. They were heavily reliant on static pre-trained data, specific input prompts, and isolated integrations like plugins or custom APIs. This presented several key limitations:

• Limited Access to Dynamic Data: Due to their dependence on user-provided or pre-trained information, these models couldn't provide real-time data or contextual information.
• Inability to Perform Actions: Operating in a closed system, AI agents could only respond to users with text. They couldn't autonomously interact with external systems. For example, an AI could explain how to write a financial report but couldn't email it internally without specific, pre-built integrations.
• Cumbersome Integrations: External integrations required custom coding, leading to high costs for each system needing its own integration, asynchronous APIs, and significant latency.

The introduction of MCP aims to resolve these issues, empowering AI Agents to retrieve and process data, and execute actions such as creating Pull Requests on GitHub, sending emails, or running financial commands.

II. What is the Model Context Protocol?

     The Model Context Protocol (MCP) is an open-source protocol introduced by Anthropic in late 2024. It aims to address a significant bottleneck in current AI systems: their ability to connect and interact with external systems, data, and tools in real time. MCP functions as a standard integration layer between AI Agents (especially Large Language Models - LLMs) and the outside world. This enables them to access dynamic data (from APIs, databases, Web3 wallets, etc.) and execute actions such as sending commands, updating statuses, or triggering processes. In essence, MCP shifts AI from a passive state to an active one, allowing it to not just respond to user prompts but also take direct action.

     Unlike traditional integration methods, which are often fragmented and require the manual creation of separate APIs for each system, MCP provides a standardized, flexible, and easily extensible framework. Its open-source and permissionless nature makes MCP particularly well-suited to the philosophy of Web3. It's already being rapidly integrated into major ecosystems like Base, BNB Chain, Solana, OpenAI Agents SDK, and various DeFi-AI applications. As AI transitions towards an Agentic model and Web3 demands secure, automated interactive systems, MCP is poised to become a foundational intermediary infrastructure for the future of both fields.

III. How the Model Context Protocol Works

     The Model Context Protocol (MCP) functions as an intermediary integration layer, connecting Large Language Models (LLMs) with external data systems or tools, spanning both Web2 and Web3 environments. The image below illustrates how MCP facilitates this interaction.

• - Host with MCP Client: This is the primary interface where users interact with the AI agent (like Claude, ChatGPT, etc.). It's where user requests are initiated and actions are determined.
• - MCP Server: This acts as the crucial bridge, processing the AI agent's requests and forwarding them to the intended destination system.
• - External System: These are the external endpoints, encompassing various entities such as databases, APIs, and cryptocurrency wallets.

     The Model Context Protocol (MCP) processes data through the following steps:

     The MCP data processing workflow unfolds as follows:

• - User Interaction: The user engages with the system using natural language. For instance, they might ask to "find the highest APY pool for USDC and stake 1000 USDC." The AI Agent receives this user command.
• - AI Agent Request Analysis: The AI Agent utilizes its LLM capabilities to comprehend the request and identify the necessary steps for action. In this example, it would determine the need to call an API to fetch the APY, followed by sending a staking command.
• - Request to MCP Server: The AI Agent then sends a request to the MCP Server, in this case, querying for the "highest APY for USDC."
• - MCP Server Routing and Processing: The MCP Server leverages Adapters (akin to plugins) to call the corresponding API or communicate directly with the target system.
• - System Response and Subsequent Actions: The MCP Server collects the data and returns it to the AI Agent (e.g., "The highest APY for USDC is 7.4% on Aave"). The AI Agent then receives this data and proceeds with the next part of the request, which is "staking 1000 USDC into Aave." The MCP Server subsequently receives this new command, calls the relevant Smart Contract Staking function, and executes the staking of 1000 USDC into Aave.

      By integrating with the MCP Server, AI Agents significantly expand their operational scope and gain the ability to execute a wide range of actions in real-time. This delivers substantial benefits to users and projects within the crypto market.

IV. The Emerging Model Context Protocol Ecosystem

     Given its immense potential, it's clear that many major projects and organizations are swiftly integrating MCP into their products. OpenAI, a leading AI company, has already incorporated MCP into its Agents SDK, enabling agents to connect with the MCP Server. Similarly, Perplexity AI, an AI Assistant developer, has launched Perplexity MCP (Sonar), allowing its AI to conduct real-time web searches via MCP.

Furthermore, numerous AI Developer/Tooling platforms have already integrated MCP, including:

• - Cursor: Integrates MCP within its IDE, enabling AI agents to interact with codebases, databases, and notification systems (e.g., playing sounds, logging events).
• - Gradio: Offers an MCP Client demo that supports MCP communication via both STDIO and SSE (event streaming).
• - Supabase: Provides an MCP server for PostgreSQL databases, allowing AI to query or update backend data.
• - Weaviate: Offers an MCP server that enables AI to perform semantic vector searches, crucial for RAG (Retrieval Augmented Generation) and AI Search Engines.

     Within the Crypto market, the Web3 community is also quick to adopt MCP. Coinbase has deployed an MCP Server for AI interaction with Smart Contracts, and BNB Chain has piloted AI Agents integrated through MCP to call contracts. The Solana community is also exploring MCP integration as a middleware layer for various projects within its ecosystem.

V. The Future and Applications of Model Context Protocol

     The emergence of MCP is transforming AI Agents from mere reactive systems into proactive entities capable of independent decision-making. MCP is essentially becoming the "USB for AI," providing AI with a rich data source to perform a multitude of diverse operations. As MCP continues to develop and mature, it promises numerous applications:

• - In Graphic Design: MCP could enable ChatGPT or Claude to directly interface with software like Blender, allowing for 3D model creation from prompts, or facilitating AI in reading Figma design files and seamlessly converting them to HTML/CSS. This will undoubtedly significantly boost the efficiency of designers and frontend developers.
• - In Geospatial Data and Mapping: Through MCP, Claude could directly interact with QGIS, an open-source software for maps and geographical data. This would empower AI Agents to autonomously analyze and map urban, agricultural, and environmental data. For example, an AI Agent could map coffee-growing regions in Vietnam based on temperature and altitude data.
• - In Web Access & Open Data: MCP will allow AI Agents to perform real-time web searches, moving beyond reliance on only pre-trained data. This will enable users and various projects to leverage AI for in-depth research and market analysis. For instance, an AI Agent could find all updates on Donald Trump's reciprocal tax policies and return the latest results.

     For the Crypto market, the advent of MCP will amplify the power of AI Agent applications within Web3. The vast data landscape of Web3 will be accessible to AI Agents, enabling them to learn autonomously and make insightful decisions that even users might not have foreseen.

     AI-Powered DeFi Automation: MCP empowers AI Agents to directly interact with DeFi protocols, thereby automating financial tasks such as: identifying the highest APY pools, rebalancing portfolios based on market fluctuations, and optimizing Yield Farming and Staking strategies.

     On-chain Data Analysis: AI Agents can leverage MCP to access real-time blockchain data. Examples include: monitoring wallet activity, analyzing Memecoin trends, depeg events, and token flow. Furthermore, on-chain data can be aggregated to serve as a basis for risk warnings.

     DAO Governance: MCP assists AI Agents in: summarizing proposals, contextual voting based on user preferences, and monitoring and analyzing DAO Treasuries. This reduces the burden on DAO members for reading and analyzing proposals, enabling quicker voting aligned with individual or organizational views.

... (and many more potential applications)

VI. Conclusion

     Overall, the emergence of MCP acts as a crucial enabler for AI Agents, allowing them to further harness their inherent power. This article aims to provide comprehensive insights into MCP, and I hope you've gained valuable knowledge from it.

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