This platform is an MCP-enabled, agentic AI system for healthcare decision support that helps consumers make better provider and insurance choices through personalized, cost-aware recommendations. It uses off-the-shelf large language models (LLMs), orchestrated through the Model Context Protocol (MCP), to combine contextual patient signals with structured healthcare data and produce explainable guidance. The system supports high-impact workflows such as provider discovery and matching, appointment-oriented navigation, and insurance plan comparison using public price-transparency data and total-cost-of-care estimates. By jointly evaluating factors like provider fit, network participation, distance, quality indicators, and expected cost, it delivers recommendations that are practical for both care quality and affordability. A core goal is rigorous performance evaluation through statistical analysis of recommendation accuracy, financial impact, and model invocation cost. The implementation stack centers on React for frontend experiences, Python or Node for backend orchestration, cloud or virtual-server deployment, GitHub for source control, and GitHub Actions for CI/CD under agile development practices.