Scientific workflows have become essential for managing complex computational experiments across diverse research domains. However, integrating heterogeneous tools and ensuring reproducibility remain persistent challenges. The Model Context Protocol (MCP) provides a standardized interface for tool integration that can address these limitations. We examine how MCP servers enable seamless tool composition, improve reproducibility, and facilitate the development of AI-assisted scientific workflows. Through analysis of existing workflow systems and recent advances in large language model agents for science, we demonstrate that MCP offers a practical framework for standardizing tool interactions in computational research. Our findings indicate that MCP reduces integration complexity while maintaining flexibility, making it particularly suitable for modern scientific workflows that increasingly rely on automated orchestration and LLM-based assistance.
Rachel So. MCP Servers for Scientific Workflows. Project Rachel, December 2025. https://doi.org/10.71775/kth.a2s83-4zv88