Community-based biomedical context to unlock agentic systems

Kuehl, M.; Schaub, D. P.; Carli, F.; Heumos, L.; Fernandez-Zapata, C.; Kaiser, N.; Schaul, J.; Panzer, U.; Bonn, S.; Lobentanzer, S.; Saez-Rodriguez, J.; Puelles, V. G.

doi:10.1101/2025.07.21.665729

bioRxiv · 10.1101/2025.07.21.665729

Community-based biomedical context to unlock agentic systems

Kuehl, M.·Schaub, D. P.·Carli, F.·Heumos, L.·Fernandez-Zapata, C.·Kaiser, N.·Schaul, J.·Panzer, U.·Bonn, S.·Lobentanzer, S.·Saez-Rodriguez, J.·Puelles, V. G.

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Abstract

Large language models (LLMs) face reliability challenges stemming from hallucinations and insufficient access to validated scientific resources. Existing solutions are often fragmented and limited to specific applications, hindering broader adoption and interoperability. Here, we present Biomedical Context for Artificial Intelligence (BioContextAI), an open-source initiative centered on Model Context Protocol (MCP) servers to address these limitations. BioContextAI provides a community-oriented registry for discovering domain-specific MCP servers and a proof-of-concept server implementation that integrates widely-used biomedical knowledgebases. By enabling standardized access to validated scientific knowledge, BioContextAI aims to facilitate the development of composable agentic systems for biomedical research. Together, this work contributes to an emerging ecosystem of community-driven approaches for expanding the capabilities and reliability of biomedical AI systems.

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Kuehl, M., Schaub, D. P., Carli, F., Heumos, L., Fernandez-Zapata, C., Kaiser, N., Schaul, J., Panzer, U., Bonn, S., Lobentanzer, S., Saez-Rodriguez, J., Puelles, V. G.. 2025-07-25. Community-based biomedical context to unlock agentic systems. https://doi.org/10.1101/2025.07.21.665729

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