Community-based biomedical context to unlock agentic systems
Authors:
- Malte KuehlMKMalte Kuehl
- Darius P. SchaubDSDarius P. Schaub
- Francesco CarliFCFrancesco Carli
- Lukas HeumosLHLukas Heumos
- Camila Fernandez-ZapataCFCamila Fernandez-Zapata
- Nico KaiserNKNico Kaiser
- Jonathan SchaulJSJonathan Schaul
- Ulf PanzerUPUlf Panzer
- Stefan BonnSBStefan Bonn
- Sebastian LobentanzerSLSebastian Lobentanzer
- Julio Saez-RodriguezJSJulio Saez-Rodriguez
- Victor G. PuellesVPVictor G. Puelles
Preprint
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.Keywords
Funding Information
- Novo Nordisk Foundation (NNF21OC0066381)
- Deutsche Forschungsgemeinschaft (SFB 1192)
- BMBF (e:MED Consortium Fibromap)
- Deutsche Forschungsgemeinschaft (FOR 5068 P9)