Model Context Contracts - MCP-Enabled Framework to Integrate LLMs With Blockchain Smart Contracts

• URL: http://arxiv.org/abs/2510.19856v1
• Date: Tue, 21 Oct 2025 19:28:20 GMT
• Title: Model Context Contracts - MCP-Enabled Framework to Integrate LLMs With Blockchain Smart Contracts
• Authors: 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,
• Abstract summary: "MCC: Model Context Contracts" is a novel framework that enables AI agents to interact directly with blockchain smart contracts.<n>Within this proposed architecture, blockchain smart contracts can function as intelligent agents capable of recognizing user input in natural language and executing the corresponding transactions.<n>This research represents the first approach to using the concept of Model Context Protocol to integrate LLMs with blockchain.
• Score: 3.8304325298697464
• License: http://creativecommons.org/licenses/by/4.0/
• 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 between users and blockchain networks. Essentially, it empowers users to interact with blockchain systems and perform transactions using queries in natural language. Within this proposed architecture, blockchain smart contracts can function as intelligent agents capable of recognizing user input in natural language and executing the corresponding transactions. To ensure that the LLM accurately interprets natural language inputs and maps them to the appropriate MCP functions, the LLM was fine-tuned using a custom dataset comprising user inputs paired with their corresponding MCP server functions. This fine-tuning process significantly improved the platform's performance and accuracy. To validate the effectiveness of MCC, we have developed an end-to-end prototype implemented on the Rahasak blockchain with the fine-tuned Llama-4 LLM. To the best of our knowledge, this research represents the first approach to using the concept of Model Context Protocol to integrate LLMs with blockchain.

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