Modern companies generate large volumes of data, but often internal users find it challenging to quickly figure out answers to their questions due to the lack of specialized knowledge. Apache Kafka is a powerful tool for real-time data processing, and many organizations use Kafka to enable self-service access to data streams. However, getting specific insights from these streams often requires specialized knowledge. Recently, I explored leveraging Cursor, an AI coding assistant, and Model Control Protocol (MCP), an open standard for integrating large language model applications and data sources, to interact with a Kafka topic hosted on Confluent Cloud. This combination made it easy to query the relevant Kafka topics with natural language, making information more accessible to a variety of users. By combining AI assistants like Cursor with integration frameworks like MCP, we can build intuitive interfaces over powerful backend systems, translating natural language requests into executable queries like Flink SQL for Kafka, unlocking data accessibility and dramatically reducing the time from request to insight.