Worked on the docker/mcp-registry repository to deliver a configuration enhancement focused on improving data sampling control for Neo4j queries. Introduced a schema sample size parameter using YAML, allowing teams to fine-tune how much data is sampled during query execution. This addition supports more reliable analytics and testing workflows by enabling targeted performance planning and tuning. Leveraging skills in configuration management and database management, the work provided a production-ready solution for adjusting sampling size directly within the configuration. The feature addressed the need for precise control over query sampling, enhancing the flexibility and reliability of analytics workloads in Neo4j environments.