Multi-Specialist Retrieval for Water Science: MCP-Enabled Evidence Access Across Hydrology Domains

Faculty-advised research — team forming

→ Core contributors included as co-authors

Environmental AI

Multi-specialist retrieval architecture adapted for water and hydrology domains. Extends the ClinicalSearch evidence access framework to water science literature with Model Context Protocol integration.

RAGMulti-AgentWater ScienceHydrologyMCPEvidence RetrievalEnvironmental AI

The crisis

Water scarcity affects over 2 billion people — evidence-based water management decisions require access to hydrology literature that most regional practitioners cannot retrieve
Flood early warning, contamination response, and water governance all depend on rapid evidence synthesis that current tools do not support
The intersection of agentic AI and water science has fewer than 10 published papers — the field is open for a working multi-specialist deployment

About this research

Water science literature spans hydrology, engineering, governance, and climate domains — evidence retrieval across these specialisms requires the same multi-agent architecture that improves clinical evidence access. This thread adapts the ClinicalSearch specialist retrieval framework to water domains, adding MCP integration for structured knowledge exchange and carbon cost tracking from the CCI framework. MDPI Water (IF 3.0) invited submission following prior MDPI publications.