Model Context Contracts - MCP-Enabled Framework to Integrate LLMs With Blockchain Smart Contracts
Eranga Bandara, Sachin Shetty, Ravi Mukkamala, Ross Gore, Peter Foytik, Safdar H. Bouk, Abdul Rahman, Xueping Liang, Ng Wee Keong, Kasun De Zoysa, Aruna Withanage, Nilaan Loganathan

TL;DR
This paper introduces MCC, a novel framework enabling large language models to interact directly with blockchain smart contracts via a standardized protocol, enhancing dynamic, context-aware blockchain interactions.
Contribution
It presents the first integration of LLMs with blockchain smart contracts using the Model Context Protocol, including a fine-tuning method for improved accuracy.
Findings
Successful prototype on Rahasak blockchain
Enhanced LLM accuracy with custom fine-tuning
First to use MCP for LLM-blockchain integration
Abstract
In recent years, blockchain has experienced widespread adoption across various industries, becoming integral to numerous enterprise applications. Concurrently, the rise of generative AI and LLMs has transformed human-computer interactions, offering advanced capabilities in understanding and generating human-like text. The introduction of the MCP has further enhanced AI integration by standardizing communication between AI systems and external data sources. Despite these advancements, there is still no standardized method for seamlessly integrating LLM applications and blockchain. To address this concern, we propose "MCC: Model Context Contracts" a novel framework that enables LLMs to interact directly with blockchain smart contracts through MCP-like protocol. This integration allows AI agents to invoke blockchain smart contracts, facilitating more dynamic and context-aware interactions…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Artificial Intelligence in Law · Big Data and Digital Economy
