A SECURE AND SCALABLE AI FRAMEWORK INTEGRATING MODEL CONTEXT PROTOCOL (MCP) FOR AUTONOMOUS RESEARCH ASSISTANCE

Abstract

The rapid growth of scientific literature and the increasing complexity of research activities have created a demand for intelligent systems capable of providing accurate, secure, and context-aware research assistance. Traditional search platforms and standalone conversational AI models often struggle to deliver reliable information due to fragmented workflows, limited contextual understanding, and dependence on static pretrained knowledge. This paper presents a Secure and Scalable AI Framework Integrating Model Context Protocol (MCP) for Autonomous Research Assistance, designed to streamline the research process through intelligent workflow orchestration and context-aware knowledge retrieval. The proposed framework integrates RetrievalAugmented Generation (RAG), semantic search, vector embeddings, and Model Context Protocol (MCP)-based communication to enable seamless interaction between large language models and specialized backend services. Research documents are processed through an automated pipeline comprising document parsing, text chunking, embedding generation, semantic indexing, and intelligent retrieval to generate responses grounded in relevant research content. Secure user authentication, modular service coordination, and a multi-database architecture ensure reliable access control, efficient data management, and scalable system performance while supporting autonomous execution of research-oriented tasks such as literature exploration, document summarization, contextual question answering, and report generation. The framework is implemented using React for the frontend, FastAPI for backend services, ChromaDB for semantic retrieval, PostgreSQL and MongoDB for structured and unstructured data management, Redis for caching, and Groq-powered large language models for high-speed inference. Experimental evaluation demonstrates that the proposed framework improves retrieval accuracy, response relevance, system scalability, and research productivity compared with conventional AI-assisted research approaches. The proposed framework illustrates how the integration of Model Context Protocol, retrievalaugmented intelligence, and modular AI services can provide a secure, scalable, and efficient platform for autonomous research assistance