# mckinsey/vizro-mcp [Health: Active]

**Category:** 🧮 Data Science Tools  
**Repository:** https://github.com/mckinsey/vizro/tree/main/vizro-mcp  
**GitHub Stars:** 3789  
**Views:** 5  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/mckinsey-vizro-mcp

## Description
Tools and templates to create validated and maintainable data charts and dashboards.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):

```json
"mcpServers": {
  "vizro-mcp": {
    "command": "uvx",
    "args": ["vizro"]
  }
}
```

## Documentation

## What mckinsey/vizro-mcp MCP server does

mckinsey/vizro-mcp MCP server is associated with Vizro, an open-source Python toolkit for building data visualization applications. The supplied description identifies its purpose as providing tools and templates for validated, maintainable charts and dashboards. It is suited to work that benefits from reusable dashboard structures and configuration-driven development.

Vizro applications can contain multiple pages and organize visual content through high-level concepts such as components, controls, actions, layouts, and navigation. Components include charts, tables, cards, KPI indicators, and forms. Controls support filters and parameters through selectors such as dropdowns and sliders. Actions can connect components, support drill-through behavior, and provide export functionality.

## How it works

The dashboard definition is expressed as configuration rather than requiring every visual and page behavior to be implemented manually. Supported configuration formats include Pydantic models, JSON, YAML, and Python dictionaries. This gives teams options for representing dashboard specifications in a typed model, a data file, or ordinary Python code.

The mckinsey/vizro-mcp MCP server can therefore fit workflows where an agent turns a visualization request into structured Vizro configuration or uses provided templates as a starting point. The source material does not enumerate the server’s individual MCP tool names or describe a separate data-ingestion mechanism, so it should not be treated as a database connector based on this listing alone.

Vizro also supports optional higher-code extensions. Developers can add custom visual formatting and custom components using Python, JavaScript, HTML, or CSS, while retaining the configuration-based application structure.

## Tools and capabilities

The documented scope supports:

- Creating chart and dashboard configurations.
- Starting from templates for common visualization application structures.
- Defining charts, tables, cards, KPI indicators, forms, filters, and parameters.
- Describing component interactions, drill-throughs, and exports.
- Arranging content with grid layouts or flexible containers.
- Configuring application navigation, including nested page hierarchies.
- Representing configuration with Pydantic, JSON, YAML, or Python dictionaries.
- Extending applications with custom formatting and components.

The repository material does not provide a complete MCP tool catalog, argument schema, or example agent conversation. Developers should inspect the project documentation before depending on a particular tool name or invocation format.

## Limitations and notes

mckinsey/vizro-mcp MCP server is focused on producing Vizro-oriented visualization applications. The supplied material does not establish support for a particular database, warehouse, hosted dashboard service, authentication provider, or deployment platform. Vizro itself is Python-based and uses Plotly, Dash, and Pydantic as trusted open-source dependencies, but those references do not imply that this MCP server independently exposes each dependency as an MCP integration.

The project is licensed under Apache 2.0 according to the repository badges. Vizro is described as open source. Advanced customization remains available through code extensions, so the low-code configuration model does not prevent developers from adding application-specific behavior.

_Full upstream README: https://allmcps.com/mcp/mckinsey-vizro-mcp/readme_