Designing a Bangla conversational AI agent for maternal health using model context protocol

Abstract

Maternal health in Bangladesh faces persistent challenges, including limited access to skilled providers, informational gaps, and stigma around perinatal mental health. Prior studies show that digital health tools remain constrained by generic content, English-dominated design and neglect of maternal mental health, limiting trust and engagement. We present Baby and Me; the first Model Context Protocol-enabled agentic AI system designed for maternal health in Bangladeshi contexts. The system delivers personalized, empathetic guidance in Bangla and Banglish by combining retrieval-augmented generation with a clinically curated knowledge base, web search, and conversational memory. A survey with 72 women revealed frequent worries about miscarriage, anxiety, and mood changes, highlighting the need for empathetic, accessible support. Evaluation of our prototype showed high contextual accuracy, low hallucination, and strong user satisfaction, with participants valuing empathy and trust while requesting greater personalization. Our findings extend human-AI interaction research by demonstrating how culturally grounded, agentic AI can serve not only as an informational tool but also as a relational companion, offering design insights for equitable health technologies. It paves the way for scalable interventions in low-resource settings, with future directions to enhance maternal outcomes. The chatbot can be accessed in this link.