AI-Assisted Data Engineering Workflows Using Model Context Protocol (Mcp), Evaluated Through A Real-World Marketplace Analytics Case Study

The growth of data-intensive e-commerce platforms has increased the complexity of analytical workflows, particularly in emerging markets where engineering resources are constrained. Traditional SQL-centric analytics often suffer from slow iteration, schema-related errors, and fragmented tooling. This paper presents a technical evaluation of the Model Context Protocol (MCP) integrated into the Cursor IDE, enabling AI-assisted, execution-aware data engineering workflows for ClickHouse-based analytical systems.
Using a production dataset of 3,000 active sellers from a large-scale public e-commerce marketplace dataset, I evaluate both engineering efficiency and analytical outcomes through a marketplace segmentation case study. Results show that MCP-assisted workflows reduce query development and debugging time by 70–80%, improve analytical reproducibility, and significantly reduce SQL errors. The case study further reveals that fast-growing sellers represent 32% of sellers while contributing 61.7% of total GMV, demonstrating the value of growth-based analytics. The findings highlight MCP’s potential as a practical protocol for AI-native data engineering in large-scale, real-world analytical environments.