MCP(모델 콘텍스트 프로토콜) 앱으로 ES|QL 및 Elastic Chart를 사용하여 AI 조수가 Kibana 대시보드를 구축할 수 있습니다. 자연적인 언어를 통해 시각화를 만들고, Kibana의 그리드 레이아웃에 살고, 그리고 실제 렌즈 대시보드로 Kibana에 직접 수출.

Clarude Desktop의 스크린 샷: MCP 앱은 채팅 내부의 대화형 대시보드를 렌더링합니다.

무엇 그것

특징

• Natural language 대시보드 생성 — 원하는 것을 설명, AI는 그것을 구축
• **Deep 데이터 분석 ** - 집계, 표면 패턴을 실행하는 오픈 엔드 탐험 흐름, 주요 발견에 대한 차트를 구축하고, 드릴 다운 쿼리를 제안 ( "내 X를 해소 같은 프롬프트에 의해 트리거)
• ES|QL 전원 - 모든 쿼리 사용 ES|QL 데이터 검색
• ** 인라인 대시보드 미리보기 ** - MCP Apps를 통해 채팅에서 직접 풀 인터렉티브 대시보드
• ** Kibana 그리드 레이아웃 ** - Kibana 대시보드와 같은 48-column 드래그 앤 드롭 그리드
• **Borealis 테마 ** - Kibana의 최신 시각 디자인 일치
• Collapsible sections - 그룹으로 패널 구성
• **Kibana에 수출 ** - 렌즈 시각화와 실제 Kibana 대쉬보드를 만듭니다

example-mcp-dashbuilder

An MCP (Model Context Protocol) app that lets AI assistants build Kibana dashboards using ES|QL and Elastic Charts. Create visualizations through natural language, preview them live with Kibana’s grid layout, and export directly to Kibana as real Lens dashboards.

Screenshot from Claude Desktop: the MCP app renders the interactive dashboard inside the chat.

What it does

You (in an MCP client) → "Build me an ecommerce analytics dashboard"
    ↓
AI explores your Elasticsearch data via ES|QL
    ↓
Creates charts (bar, line, pie, metric, heatmap) with Elastic Charts
    ↓
Interactive dashboard rendered inline in the chat (MCP Apps)
    ↓
One-click export to Kibana as Lens visualizations

Features

• Natural language dashboard creation — describe what you want, the AI builds it
• Deep data analysis — open-ended exploration flow that runs aggregations, surfaces patterns, builds charts for key findings, and suggests drill-down queries (triggered by prompts like “analyze my X”)
• ES|QL powered — all queries use ES|QL for data retrieval
• Inline dashboard preview — full interactive dashboard rendered directly in the chat via MCP Apps
• Kibana grid layout — same 48-column drag-and-drop grid as Kibana dashboards
• Borealis theme — matches Kibana’s latest visual design
• Collapsible sections — organize panels into groups
• Export to Kibana — creates real Kibana dashboards with Lens visualizations
• Import from Kibana — import existing ES|QL-based Kibana dashboards for AI-assisted editing
• Custom color themes — apply custom palettes to charts, heatmaps, and metrics
• Time picker — filter data by time range with automatic time field detection
• Multiple dashboards — create, switch between, and manage multiple dashboards
• Session isolation — parallel chat conversations work on separate dashboards via dashboardId threading
• Elastic Cloud support — works with local Elasticsearch and Elastic Cloud (Cloud ID + API key)
• Server instructions — workflow, tips, and capabilities exposed to every MCP client via the initialize response
• Dataviz best practices — built-in guidelines for chart selection and dashboard composition
• ES|QL reference — built-in language reference for correct query syntax

Architecture

For a structured walkthrough with Mermaid diagrams (system context, data flows, monorepo build order), see ARCHITECTURE.md.

Prerequisites

• Node.js 22+
• Elasticsearch (local or Elastic Cloud)
• Kibana (for export/import)
• An MCP client: Cursor (v2.6+ for MCP Apps inline preview), Claude Desktop, Claude Code, or VS Code Copilot

Quick install