MCP 服务器

vizro-mcp

Tools

get_sample_data_info

If user provides no data, use this tool to get sample data information. Use the following data for the below purposes: - iris: mostly numerical with one categorical column, good for scatter, histogram, boxplot, etc. - tips: contains mix of numerical and categorical columns, good for bar, pie, etc. - stocks: stock prices, good for line, scatter, generally things that change over time - gapminder: demographic data, good for line, scatter, generally things with maps or many categories Args: data_name: Name of the dataset to get sample data for Returns: Data info object containing information about the dataset.

validate_model_config

Validate Vizro model configuration. Run ALWAYS when you have a complete dashboard configuration. If successful, the tool will return the python code and, if it is a remote file, the py.cafe link to the chart. The PyCafe link will be automatically opened in your default browser if auto_open is True. Args: config: Either a JSON string or a dictionary representing a Vizro model configuration data_infos: List of DFMetaData objects containing information about the data files auto_open: Whether to automatically open the PyCafe link in a browser Returns: ValidationResults object with status and dashboard details

get_model_json_schema

Get the JSON schema for the specified Vizro model. Args: model_name: Name of the Vizro model to get schema for (e.g., 'Card', 'Dashboard', 'Page') Returns: JSON schema of the requested Vizro model

get_vizro_chart_or_dashboard_plan

Get instructions for creating a Vizro chart or dashboard. Call FIRST when asked to create Vizro things.

load_and_analyze_data

Load data from various file formats into a pandas DataFrame and analyze its structure. Supported formats: - CSV (.csv) - JSON (.json) - HTML (.html, .htm) - Excel (.xls, .xlsx) - OpenDocument Spreadsheet (.ods) - Parquet (.parquet) Args: path_or_url: Local file path or URL to a data file Returns: DataAnalysisResults object containing DataFrame information and metadata

validate_chart_code

Validate the chart code created by the user and optionally open the PyCafe link in a browser. Args: config: A ChartPlan object with the chart configuration data_info: Metadata for the dataset to be used in the chart auto_open: Whether to automatically open the PyCafe link in a browser Returns: ValidationResults object with status and dashboard details

README

Vizro is a low-code toolkit for building high-quality data visualization apps

Documentation | Get Started | Vizro examples gallery

What is Vizro?

Vizro is an open-source Python-based toolkit.

Use it to build beautiful and powerful data visualization apps quickly and easily, without needing advanced engineering or visual design expertise.

Then customize and deploy your app to production at scale.

<div align="center"> <img src="https://raw.githubusercontent.com/mckinsey/vizro/main/.github/images/code_dashboard.png" width="100%"/> Use a few lines of simple low-code configuration, with in-built visual design best practices, to assemble high-quality multi-page prototypes. </div> <br>

The benefits of the Vizro toolkit include:

Visit our "How-to guides" for a more detailed explanation of Vizro features.

Why use Vizro?

Vizro helps you to build data visualization apps that are:

Quick and easy

Build apps in minutes. Use a few lines of simple configuration (via Pydantic models, JSON, YAML, or Python dictionaries) in place of thousands of lines of code.

Beautiful and powerful

Build high-quality multi-page apps without needing advanced engineering or visual design expertise. Use powerful features of production-grade BI tools, with in-built visual design best practices.

Flexible

Benefit from the capabilities and flexibility of open-source packages. Use the trusted dependencies of Plotly, Dash, and Pydantic.

Customizable

Almost infinite control for advanced users. Use Python, JavaScript, HTML and CSS code extensions.

Scalable

Rapidly prototype and deploy to production. Use the in-built production-grade capabilities of Plotly, Dash and Pydantic.

Visit "Why should I use Vizro?" for a more detailed explanation of Vizro use cases.

When to use Vizro?

Use Vizro when you need to combine the speed and ease of low-code Python tools, with production capabilities of JavaScript and BI tools, and the freedom of open source:

Have an app that looks beautiful and professional by default.
Enjoy the simplicity of low-code, plus the option to customize with code almost infinitely.
Rapidly create prototypes which are production-ready and easy to deploy at scale.

How to use Vizro?

Vizro framework

Low-code framework for building dashboards.

The Vizro framework underpins the entire Vizro toolkit. It is a Python package (called vizro).

Visit the documentation for more details.

Vizro visual vocabulary

Chart examples.

The visual vocabulary helps you to decide which chart type to use for your requirements, and offers sample code to create these charts with Plotly or embed them into a Vizro dashboard.

Visit the visual vocabulary to search for charts or get inspiration.

Vizro examples gallery

Dashboard examples.

The dashboard examples gallery enables you to explore Vizro in action by viewing interactive example apps. You can copy the code to use as a template or starter for your next dashboard.

Visit the dashboard examples gallery to see the dashboards in action.

Vizro-AI

Use LLMs to generate charts and dashboards.

Vizro-AI is a separate package (called vizro_ai) that extends Vizro to incorporate LLMs. Use it to build interactive Vizro charts and dashboards, by simply describing what you need in plain English or other languages.

Visit the Vizro-AI documentation for more details.

Installation and first steps

pip install vizro

See the installation guide for more information.

The get started documentation explains how to create your first dashboard.

Packages

This repository is a monorepo containing the following packages:

Folder	Version	Documentation
vizro-core		Vizro Docs
vizro-ai		Vizro-AI Docs

Community and development

We encourage you to ask and discuss any technical questions via the GitHub Issues. This is also the place where you can submit bug reports or request new features.

Want to contribute to Vizro?

The contributing guide explains how you can contribute to Vizro.

You can also view current and former contributors here.

Want to report a security vulnerability?

See our security policy.

License

vizro is distributed under the terms of the Apache License 2.0.