Personal AI Research Agent Using RAG And MCP

The rapid expansion of academic literature and digital research resources has significantly increased the complexity of modern research activities. Researchers are required to navigate large volumes of documents across multiple platforms, often leading to fragmented workflows and difficulties in maintaining contextual continuity over extended research periods. While Large Language Models (LLMs) have improved natural language interaction with research content, standalone LLM-based approaches often suffer from limitations such as hallucinations, weak factual grounding, and lack of persistent memory, reducing their effectiveness in document-centric research tasks. Recent advancements have introduced hybrid frameworks that augment LLMs with external retrieval and orchestration mechanisms to address these challenges. This review paper examines key developments in Retrieval-Augmented Generation (RAG) and orchestration frameworks such as the Model Context Protocol (MCP), focusing on their application in AI-assisted research systems. RAG enhances response reliability by grounding language model outputs in relevant source documents, while MCP enables structured coordination between LLMs and external tools, including web search and summarization services. The review also highlights the growing importance of persistent memory mechanisms for supporting long-term research continuity and cumulative knowledge building. By synthesizing findings from recent studies, this paper identifies common architectural patterns and design principles adopted in modern AI-driven research assistants. The analysis discusses the strengths and limitations of existing approaches with respect to retrieval quality, context management, scalability, and security. Additionally, open challenges related to standardized evaluation, long-term memory management, and secure tool orchestration are highlighted. Overall, this review provides a consolidated perspective on current practices and emerging trends in AI- assisted research systems, offering insights that can guide future research and development in this rapidly evolving field.