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mckinsey

vizro-mcp

Official
by mckinsey
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Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_vizro_chart_or_dashboard_planA

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.

Returns:
    Instructions for creating a Vizro chart or dashboard
get_model_json_schemaA

Get the JSON schema for the specified Vizro model. Server Vizro version: 0.1.62

get_sample_data_infoA

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

Returns:
    Data info object containing information about the dataset.
load_and_analyze_dataA

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)

Returns:
    DataAnalysisResults object containing DataFrame information and metadata
validate_dashboard_configA

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.

Returns:
    ValidationResults object with status and dashboard details
validate_chart_codeB

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

Returns:
    ValidationResults object with status and dashboard details

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

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 6 tools

Disambiguation4/5

Most tools have distinct purposes (schema retrieval, sample data, planning, data loading, validation). The two validate_* tools overlap somewhat (chart code vs dashboard config), and get_model_json_schema vs get_vizro_chart_or_dashboard_plan could be confused, but descriptions clarify intent.

Naming Consistency5/5

All six tools follow a consistent snake_case verb_noun pattern (get_*, load_*, validate_*). The long get_vizro_chart_or_dashboard_plan name is verbose but still follows the established convention.

Tool Count5/5

Six tools is well-scoped for a Vizro dashboard-building helper. Each tool covers a distinct phase of the workflow (plan, schema, data, validate), so none feels redundant.

Completeness4/5

The surface covers the full authoring lifecycle: planning, schema lookup, data loading, and validation. A tool to render/serve the finished dashboard directly (beyond validation output) would close the last gap.

Maintenance

ActivityActive
ResponsivenessUnresponsive

Resources