VFB-MCP: Natural-Language Access to Drosophila Neuroscience Grounded by an Expert-Curated Ontology-Led Knowledgebase

Biological databases store curated knowledge that researchers traditionally access through web interfaces or APIs. To move beyond casual browsing requires domain-specific knowledge and expertise to frame the queries necessary to explore this data. This generates a barrier for new users in scientific fields undergoing paradigm shifts. Exposing these databases to large language models (LLMs) via the Model Context Protocol (MCP) enables natural-language access, a potential accessibility solution. We implement this for Virtual Fly Brain (VFB), an expert-curated and ontology-backed knowledgebase of Drosophila neuroscience, providing the precision needed to make recently-integrated connectomes accessible. Benchmarked on 30 neuroscience tasks against a bare LLM and a web-search-assisted LLM, the VFB-MCP-equipped LLM produces precise, verifiable and appropriately quantified answers on 25/30 tasks vs 14/30 for web and 2/30 for bare (Wilcoxon p<0.01, Holm-corrected, all pairwise comparisons). The MCP advantage is largest for tasks where data quantification is required (89% vs 11% web). This work establishes MCP over ontology-backed knowledge graphs as an effective method to improve LLM response quality for neuroscience and connectomics data.