This is the official Tableau MCP server, a TypeScript application that bridges AI assistants with Tableau Cloud, Tableau Server, and Tableau Desktop via the Model Context Protocol (MCP). It exposes a comprehensive set of tools covering content exploration (workbooks, views, datasources), administration (users, flows, extract refresh tasks, jobs, license reclamation), advanced analytics (Pulse metrics, insights, knowledge graph), and human-in-the-loop confirmation workflows. The server is available in three variants: a web variant running over HTTP with OAuth 2.1 support (PKCE, token rotation, client registration), a desktop variant communicating with the local Tableau Desktop Agent over stdio, and a combined variant. Its architecture is modular: a core server layer registers tools and prompts against an MCP SDK instance, tools delegate to a Zodios-based Tableau REST API client or the desktop agent API, and cross-cutting concerns like authentication, feature gates, telemetry, and session stores are provided through pluggable provider interfaces. The codebase demonstrates strong engineering practices with extensive unit/e2e tests, typed schemas via Zod, consistent error handling patterns, and a clear separation between web and desktop concerns.
DataEase is a self-hosted, open-source business intelligence (BI) platform that enables users to connect to diverse data sources, build datasets, create interactive charts and dashboards, and share insights via links or embedded widgets. The codebase is a full-stack monolith with a Java Spring Boot backend (JPA, Calcite SQL parser, Quartz scheduling) and a Vue 3 frontend (AntV G2/G2Plot/L7 for charts, UniverJS for spreadsheets). It supports a wide range of database connectors (MySQL, PostgreSQL, Oracle, ClickHouse, StarRocks, Elasticsearch, MongoDB, etc.), dataset types (SQL, union, custom fields), chart types (bar, line, pie, map, table, indicator, etc.), and enterprise features like row/column-level permissions, scheduled sync, report delivery, and multi-factor authentication. The architecture includes an SDK layer for extensions (datasource providers, sync providers, chart plugins) and a separate Xpack module for commercial features. The platform is deployed via Docker Compose with APISIX gateway. The codebase is actively maintained (24k+ stars, recent commits) but exhibits some technical drift due to deep inheritance hierarchies in chart handlers, code duplication across engine providers, and tight coupling to specific library versions (AntV G2, UniverJS). Overall, it is a solid, production-grade BI tool with good separation of concerns but with room for refactoring in certain areas.
Kanaries/Rath is an open-source augmented analytics BI platform that automates exploratory data analysis, causal discovery, and dashboard creation. It is a full-stack application with a React/TypeScript frontend (Vite, MobX, shadcn/ui) and multiple Python microservices (causal inference, narrative insight generation, database connectors). The system ingests data from files, databases (PostgreSQL, ClickHouse, MySQL, etc.), and REST APIs, then provides automated visualization (mega-automation), semi-automated exploration, causal analysis (PC, FCI, GES, XLearner), and interactive dashboard building. It also includes custom Vega renderers (WebGL, Painter) for high-performance chart rendering and an Electron desktop wrapper. While primarily an application, it contains significant data research components such as causal discovery algorithms, statistical insight detection, and text pattern extraction, making it a hybrid tool for both data exploration and advanced analytics.