The Research Assistant can deliver better search results by implementing real-time AI query optimization through the Model Context Protocol (MCP). Instead of relying on static, centralized indexes, this approach enables the AI assistant to query our catalogue and other distributed content sources directly and dynamically refine searches based on what it discovers.

Users would receive not only improved results from their initial query, but also optimized search terms they can use for further manual research. This helps them learn better search techniques while finding relevant materials faster, reducing frustration and improving research outcomes. We believe the AI can be a great help in designing search queries, where users can focus on content selection, keeping the human in the loop as well.

The technical implementation using MCP servers allows the AI to execute searches in real-time, analyze results, and iteratively adjust query parameters based on actual content availability.

Source of idea: https://aarontay.substack.com/p/mcp-servers-and-academic-search-the