Implementation of Model Context Protocol for Enhanced Large Language Model Applications

Analyzing and processing natural language in a way that resonates with humans is crucial in the modern era. Therefore, developing computer understanding of this language is essential. Large language models have played a significant role in advancing natural language understanding and fostering synergy between computers and natural language. However, this synergy is limited by the inability to access specific contexts in real time, negatively impacting enhanced retrieval (RAG) generation. Furthermore, it's important to emphasize the frequent failure of single-source retrieval systems when tasked with handling user queries. This research paper implements the Model Context Protocol (MCP) within a unified framework that dynamically coordinates three sources: local file systems, web search, and vector databases. The source selection mechanism of this system features query analysis, a hybrid keyword retrieval model, and robust error correction.