DeepCore MCP protocol

P protocol architecture

DeepCore, as the largest AI Agent application building platform in WEB3, is upgraded to support the MCP protocol to access MCPServer, which provides convenient access to the massive tools of the MCP ecosystem and accelerates the process of building large model applications.

DeepCore's Core Positioning & Advantages

We focus on building Web3-native solutions through distributed system architecture and incentive models to create differentiated advantages:

1. Rapid Integration: Seamlessly connect to MCP services from third-party platforms like CoinMarketCap (CMC) and CoinGecko;

2. Protocol Standardization: Package DeepCore interfaces as high-availability MCP services for external platform adoption.

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# MCP Technical Details

I. MCP Technology Overview

• Definition: MCP (Model Context Protocol), proposed by Anthropic in 2024, is an open protocol designed to standardize interactions between AI Agents and external data/tools;

• Core Value: Provides a unified interface (USB-C analogy) to eliminate data silos and simplify AI agent-service connectivity;

• Innovation: Replaces traditional end-to-end custom encapsulation, becoming the key standard for Web3 AI Agent evolution.

MCP addresses the challenges of AI systems accessing data, replacing traditional fragmented integration approaches with a universal open standard.

II.Why DeepCore Bets on MCP Protocol

1. Solving Data & Platform Integration Challenges

• Industry Pain Points: Early Web3 AI frameworks(e.g., #AI16Z, #ARC, #Swarms) require custom APIs per data source, leading to plugin bloat and real-time data isolation, They mimicked Web2 giants’ resource monopoly models, relying on superficial narratives like “multi-agent collaboration + Tokenomics” while ignoring Web3-native architecture. True breakthroughs require:

  • Protocol-layer innovation: Decentralized data interaction standards beyond Web2-style deployment;

  • Deep research capabilities: Enabling AI agents to understand on-chain complexities (e.g., MEV hunting, cross-chain arbitrage).

• DeepCore Solution:

  • Reduce M×N integration complexity (M clients × N data sources) to M+N via MCP protocol;

  • Enable plug-and-play connectivity between AI agents and data sources using JSON-RPC client-server architecture (similar to USB-C adapters);

  • Support dynamic data access, significantly improving efficiency in automation, real-time queries, and cross-platform collaboration.

2. MCP Advantages in Web3 Environments

• Standardized Interfaces: Directly access on-chain data from Ethereum, Solana, BNB Chain, etc., without custom code (Case: Deep Research MCP server with integrated search engine);

• Efficiency Leap: Millisecond-level smart contract state and transaction data queries, adapted to crypto market volatility;

• Security Architecture: Built-in authentication and privacy protection mechanisms, compliant with Web3 security standards;

• Modular Design: Component reuse reduces maintenance costs and accelerates ecosystem iteration.

3. Practical Impact:

Legacy Challenges	MCP Solutions
Custom API required per new data source	Standardized protocol supports Ethereum, Solana, etc., eliminating redundant development
Real-time data isolation (>5min latency)	On-chain state queries with <500ms latency
High security verification cost ($1.2/request)	zk-SNARK verification at <$0.05/request

4. Web3 Advantages:

• Cross-chain standardization: Directly access multi-chain data (e.g., Solana, BNB Chain DeFi positions) without custom code;

• Dynamic risk response: Monitor smart contract state changes in real-time to trigger auto-liquidation (case: Uniswap V3 pool attack alerts);

• Modular reuse: Developers invoke prebuilt components (e.g., “on-chain sentiment analysis”), boosting efficiency by 70%.

III.The core role of Agent-Core

1. Standardized Agent Interaction Protocol:

Technical Architecture

• Employs a modular API gateway design with a unified Agent communication interface (DAI-Protocol), supporting multi-protocol adaptation for HTTP/3, WebSocket, and Libp2p

• Integrates semantic parsing middleware that automatically converts output formats across AI models (e.g., standardizing responses between GPT-4o, DeepSeek R1 and Claude-3)

• Provides a compatibility testing toolkit with 100+ predefined test cases to validate Agent interaction compliance

Platform Role

• Enables cross-chain/cross-platform interoperability: Agents on Solana can seamlessly interact with DApps on Ethereum, Base, BNB CHAIN... and other ecosystems

• Builds an open collaboration network: Developers can modularize Agent functionalities into composable "smart building blocks"

• Use Case: A DeFi strategy Agent automatically fetches Chainlink oracle data while coordinating with a transaction execution Agent on Base

1. On-Chain/Off-Chain Data Routing Layer (Oracle Hub):

Technical Breakthroughs

• Innovative hybrid verification mechanism: Combines Zero-Knowledge Proofs (zk-SNARKs) with TEE trusted execution environments to ensure data authenticity

• Constructs a layered data pipeline:

  • Layer1: Real-time synchronization of on-chain critical data (e.g., token prices, smart contract states)

  • Layer2: Caching of off-chain complex data (social media sentiment, IoT data) via decentralized storage networks

• Develops dynamic routing algorithms: Intelligently selects optimal data transmission paths based on network conditions, reducing latency by 40%

Ecosystem Value

• Supports complex application scenarios:

  • Generative AI Agents access on-chain copyright data in real-time

  • Cross-chain derivatives trading Agents synchronize multi-chain liquidity information

• Enables data assetization: Developers can package private data into tradable AI Agents through the data routing layer

1. Secure Sandbox Execution Environment:

Security Architecture

• Three-tier protection system:

  1. Hardware-Level Isolation: Built on Intel SGX trusted execution environments

  2. Smart Contract Firewall: Monitors gas consumption patterns in real-time to detect anomalies

  3. Dynamic Permission Control: Implements zero-trust architecture requiring re-authorization for each Agent interaction

• Proprietary Risk Simulation Engine: Simulates 10,000+ attack scenarios to intelligently generate defense strategies

Ecosystem Empowerment

Sparks new business models:

• DeFi scenario: AI Agents will validate each other and automatically form strategic alliances, increasing the expected revenue in the Defi domain by 300%.

• Alpha project research and judgment scenario: AI Agent group generates AIization similar to Nansan, Arkham, GMGN and other comprehensive platforms through multiple data platform

Build a self-evolving ecosystem: Collaborative networks will generate knowledge sharing events to drive continuous Agent iteration.