A recent technical post demonstrates a novel integration of the Model Context Protocol (MCP) with Elasticsearch's ES|QL query language to create AI-native Kibana dashboards. Instead of manually configuring charts and visualizations, users describe their analytical intent in natural language. The system uses MCP to allow an LLM to generate ES|QL queries dynamically, which then populate the dashboard. This approach reduces the cognitive load on analysts and enables faster, more intuitive data exploration. For developers building AI-enhanced tools, this pattern shows how to combine LLM reasoning with existing query languages to create powerful, user-friendly interfaces. The technique is particularly relevant for observability and business intelligence use cases where rapid iteration on data views is critical.
A new approach to Kibana dashboards using MCP and ES|QL for AI-native, intent-driven data visualization.