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.
McLachlan, A. D., Court, R., Pilgrim, C., Longden, K., Brown, N. H. D., Osumi-Sutherland, D., Jefferis, G. S. X. E., Armstrong, D. J.
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