Model Context Contracts - MCP-Enabled Framework to Integrate LLMs With Blockchain Smart Contracts
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2025.
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Citing papers per year: 2025: 3; 2026: 7.
10 papers.
- 2025A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflowscited by 15
- 2025Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoningcited by 14
- 2025ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applicationscited by 7
- 2026A Practical Guide to Agentic AI Transition in Organizationscited by 5
- 2026Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chainscited by 2
- 2026Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agentscited by 2
- 2026Towards an Agent-First Web: Redesigning the Web for AI Agentscited by 2
- 2026An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Tradingcited by 1
- 2026Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environmentscited by 0
- 2026BaseCamp --- An Agentic AI Framework for Automating DNA Sequencing Data Pipelinescited by 0
- 2026BaseCamp --- An Agentic AI Framework for Automating DNA Sequencing Data Pipelines23 Sep 2026
- 2026Towards an Agent-First Web: Redesigning the Web for AI Agents17 Jun 2026
- 2026Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents28 Apr 2026
- 2026Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains7 Apr 2026
- 2026Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments31 Mar 2026
- 2026An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading27 Jan 2026
- 2026A Practical Guide to Agentic AI Transition in Organizations27 Jan 2026
- 2025Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning25 Dec 2025
- 2025A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows9 Dec 2025
- 2025ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applications4 Dec 2025
Similar, not cited
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- 2025Network and Systems Performance Characterization of MCP-Enabled LLM Agents
- 2025LLM-Net: Democratizing LLMs-as-a-Service through Blockchain-based Expert Networks
- 2024LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection
- 2025Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review
- 2026Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering
- 2024Blockchain Meets LLMs: A Living Survey on Bidirectional Integration
- 2025LLM-BSCVM: An LLM-Based Blockchain Smart Contract Vulnerability Management Framework
- 2024Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration