Low-code Python toolkit plus Model Context Protocol (MCP) server (vizro-mcp) for rapidly building and deploying high-quality Plotly/Dash data-visualization dashboards.
https://github.com/mckinsey/vizroThe Vizro MCP server transforms how you build data visualization dashboards. Instead of wrestling with hundreds of lines of Plotly/Dash boilerplate, you describe your dashboard requirements in plain English to an AI, and get production-ready code instantly.
You know the drill: stakeholders want "just a quick dashboard" to visualize their data. Three weeks later, you're still debugging CSS grid layouts, fighting with Plotly callback chains, and explaining why the filter dropdown breaks on mobile.
Building dashboards shouldn't require a PhD in front-end frameworks. You have data insights to deliver, not component libraries to master.
Vizro MCP bridges the gap between natural language requests and production-ready dashboards. This isn't another code generator that spits out unmaintainable spaghetti - it creates clean, structured configurations that produce professional dashboards.
Tell it what you want:
"Create a sales dashboard with revenue trends by region,
a top products table, and filters for date range and product category"
Get working code:
pages:
- components:
- plotly.Graph:
figure: revenue_by_region_chart
- dash_table.DataTable:
data: top_products
controls:
- dcc.DatePickerRange: date_range
- dcc.Dropdown: product_category
Dashboard prototyping in minutes, not days. Skip the setup ceremony and configuration hunting. Describe your requirements and get a working prototype immediately.
Production-ready output by default. No more "proof of concept" dashboards that fall apart under real load. Every generated dashboard follows enterprise-grade patterns with proper error handling and responsive design.
Zero learning curve for stakeholders. Business users can iterate on dashboard requirements directly through natural language, eliminating the traditional back-and-forth translation between requirements and implementation.
Consistent visual design without hiring designers. McKinsey's built-in design system ensures your dashboards look professional regardless of your CSS skills.
Executive reporting pipelines: Transform weekly Excel reports into interactive dashboards. Feed your data pipeline output directly into Vizro configurations, and let executives slice and dice metrics themselves.
Data science presentation layer: Skip Jupyter notebook exports for stakeholder presentations. Generate clean, interactive dashboards that let business users explore model outputs and feature importance without touching code.
Customer-facing analytics: Build white-label analytics dashboards for SaaS products. Use the MCP server to rapidly prototype different layouts based on customer feedback, then deploy with your existing infrastructure.
Internal tool consolidation: Replace scattered Tableau/PowerBI licenses with code-managed dashboards. Version control your analytics, deploy through CI/CD, and maintain everything in your existing Python ecosystem.
Drop into existing Python projects. Install with pip install vizro-mcp and connect to any MCP-compatible AI client like Cursor or Claude Desktop. Your data processing pipelines feed into Vizro configurations without architectural changes.
Version control friendly. All dashboards are defined as YAML/JSON configurations that diff cleanly in Git. Code review dashboard changes like any other feature, and deploy through your existing CI/CD pipeline.
Extends when you need it. Start with generated configurations, then add custom Python callbacks for complex interactions. The low-code foundation doesn't lock you out of high-code customization.
Deploy anywhere. Generated dashboards are standard Dash applications. Run them locally during development, containerize for Kubernetes, or deploy to any Python hosting platform.
Connect Vizro MCP to your AI development environment and start building dashboards the way you always wished you could - by describing what you want, not how to build it.