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vizro-mcp

Official
by mckinsey

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Schema

Prompts

Interactive templates invoked by user choice

NameDescription
create_starter_dashboardPrompt template for getting started with Vizro.
create_dashboardPrompt template for creating an EDA dashboard based on one dataset.
create_vizro_chartPrompt template for creating a Vizro chart.

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Tools

Functions exposed to the LLM to take actions

NameDescription
get_vizro_chart_or_dashboard_plan

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

Must be ALWAYS called FIRST with advanced_mode=False, then call again with advanced_mode=True if the JSON config does not suffice anymore. Args: user_plan: The type of Vizro thing the user wants to create user_host: The host the user is using, if "ide" you can use the IDE/editor to run python code advanced_mode: Only call if you need to use custom CSS, custom components or custom actions. No need to call this with advanced_mode=True if you need advanced charts, use `custom_charts` in the `validate_dashboard_config` tool instead. Returns: Instructions for creating a Vizro chart or dashboard
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_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.
load_and_analyze_data

Use to understand local or remote data files. Must be called with absolute paths or URLs.

Supported formats: - CSV (.csv) - JSON (.json) - HTML (.html, .htm) - Excel (.xls, .xlsx) - OpenDocument Spreadsheet (.ods) - Parquet (.parquet) Args: path_or_url: Absolute (important!) local file path or URL to a data file Returns: DataAnalysisResults object containing DataFrame information and metadata
validate_dashboard_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: dashboard_config: Either a JSON string or a dictionary representing a Vizro dashboard model configuration data_infos: List of DFMetaData objects containing information about the data files custom_charts: List of ChartPlan objects containing information about the custom charts in the dashboard auto_open: Whether to automatically open the PyCafe link in a browser Returns: ValidationResults object with status and dashboard details
validate_chart_code

Validate the chart code created by the user and optionally open the PyCafe link in a browser.

Args: chart_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

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