Technology Background

Understanding MCP Technology and Its Significance

The Model Context Protocol (MCP), introduced by Anthropic, is an open standard that facilitates seamless integration between AI models and external data sources. It enables AI systems to access, interpret, and act upon diverse datasets efficiently, overcoming the limitations of siloed information.

Building A Blockchain-Native MCP Server

GraphAI extends the capabilities of MCP into the blockchain realm, offering:

• Structured Data Transformation: Converts fragmented blockchain data into structured knowledge graphs, making it accessible and interpretable for AI models.

• Real-Time Data Processing: Employs a cloud-native architecture that supports real-time data ingestion and processing, ensuring AI agents have up-to-date information.

• Cross-Chain Compatibility: Integrates data from multiple blockchain networks, enhancing versatility in various Web3 applications.

• Scalability: Utilizes distributed data processing to handle vast amounts of data across multiple nodes, ensuring high availability and fault tolerance.

Integrating RWAs Into Agentic AI Workflows

As Real World Assets are increasingly integrated into AI agents workflows, the need for scalable, structured data becomes critical. GraphAI serves as the foundational layer for these agentic workflows, providing the necessary data infrastructure to support complex AI-driven decision-making processes in real-time.

By positioning itself as the MCP server for blockchain data, GraphAI provides the critical infrastructure component for the next generation of RWA-enabled, AI-powered decentralized applications. It enables LLMs to access comprehensive, real-time data, facilitating more informed and efficient operations that draw upon both off-chain and on-chain assets.

Introducing The MCP-Native, Open Graph RAG

GraphAI builds on the potential offered by MCP technology by introducing our novel Open Graph RAG. The Open Graph RAG serves as the linchpin of GraphAI’s data engine: an open, graph‑native retrieval‑augmented‑generation layer that sits directly on top of the Model Context Protocol (MCP). MCP defines the standard way an AI model requests outside context; Open Graph RAG provides that context from the blockchain in a form the model can actually reason with, transforming raw, multi‑chain data into a living knowledge network of transactions, asset flows, and state changes.

By building on MCP, Open Graph RAG turns every context request into a focused interaction with GraphAI’s real‑time index. Historical blocks and live on‑chain events are continuously ingested, structured as a knowledge graph, and mirrored in high‑performance vector stores, so when an LLM or agent pings the MCP interface it receives precisely the sub‑graph and embeddings it needs—complete with provenance and timestamps—without manual data wrangling or format juggling. The result is an always‑fresh, queryable snapshot of blockchain reality that can be consumed in natural language or code, whether the task is valuing tokenized real estate, forecasting liquidation risk, or tracing the full lineage of an RWA across chains.

This seamless hand‑off between MCP and Open Graph RAG is what makes GraphAI the first data layer purpose‑built for RWA‑ready DeFAI. Agents gain the depth and context they need to automate trades, compliance checks, and portfolio management, while developers inherit an open, interoperable standard that plugs effortlessly into existing toolchains. In short, Open Graph RAG upgrades MCP from a generic conduit into a blockchain‑aware intelligence fabric, delivering the structured insight necessary to weave real‑world assets into autonomous on‑chain finance.