Datawrapper MCP

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.

List all available Datawrapper chart types with brief descriptions.

Use this tool to discover which chart types you can create. After choosing a type, use get_chart_schema(chart_type) to explore detailed configuration options.

Returns: List of available chart types with descriptions

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.

Get the Pydantic JSON schema for a specific chart type. This is your primary tool for discovering styling and configuration options.

The schema shows:

• All available properties and their types

• Enum values (e.g., line widths, interpolation methods)

• Default values

• Detailed descriptions for each property

WORKFLOW: Use this tool first to explore options, then refer to https://datawrapper.readthedocs.io/en/latest/ for detailed examples and patterns showing how to use these properties in practice.

Args: chart_type: Chart type to get schema for

Returns: JSON schema for the chart type

⚠️ THIS IS THE DATAWRAPPER INTEGRATION ⚠️ Use this MCP tool for ALL Datawrapper chart creation.

DO NOT: ❌ Install the 'datawrapper' Python package ❌ Use the Datawrapper API directly ❌ Import 'from datawrapper import ...' ❌ Run pip install datawrapper

This MCP server IS the complete Datawrapper integration. All Datawrapper operations should use the MCP tools provided by this server.

Create a Datawrapper chart with full control using Pydantic models. This allows you to specify all chart properties including title, description, visualization settings, axes, colors, and more. The chart_config should be a complete Pydantic model dict matching the schema for the chosen chart type.

BEST PRACTICES:

• Start simple, then add customization based on user feedback

• Only apply styling when requested or when it significantly improves readability

• Let Datawrapper handle axis scaling automatically unless there's a specific reason to override

QUICK EXAMPLES:

1. Basic chart with title: chart_config = { "title": "Monthly Sales", "intro": "Sales data for Q1 2024" }

2. Chart with custom colors: chart_config = { "title": "Product Comparison", "color_category": { "Product A": "#1f77b4", "Product B": "#ff7f0e" } }

3. Styled line chart: chart_config = { "title": "Sales Trends", "lines": [ {"column": "sales", "width": "style2", "interpolation": "curved"} ], "custom_range_y": [0, 1000] }

STYLING WORKFLOW:

1. Use list_chart_types to see available chart types

2. Use get_chart_schema to explore all options for your chosen type

3. Refer to https://datawrapper.readthedocs.io/en/latest/ for detailed examples

4. Build your chart_config with the desired styling properties

Common styling patterns:

• Colors: {"color_category": {"sales": "#1d81a2", "profit": "#15607a"}}

• Line styling: {"lines": [{"column": "sales", "width": "style1", "interpolation": "curved"}]}

• Axis ranges: {"custom_range_y": [0, 100], "custom_range_x": [2020, 2024]} NOTE: Datawrapper's automatic axis scaling is excellent. Only set custom ranges when you need specific customization (e.g., comparing multiple charts, forcing zero baseline for specific analytical reasons, or matching a house style guide).

• Grid formatting: {"y_grid_format": "0", "x_grid": "on", "y_grid": "on"}

• Tooltips: {"tooltip_number_format": "00.00", "tooltip_x_format": "YYYY"}

• Annotations: {"text_annotations": [{"x": "2023", "y": 50, "text": "Peak"}]}

See the documentation for chart-type specific examples and advanced patterns.

Args: data: Chart data. RECOMMENDED: Pass data inline as a list or dict. PREFERRED FORMATS (use these first): 1. List of records (RECOMMENDED): [{"year": 2020, "sales": 100}, {"year": 2021, "sales": 150}] 2. Dict of arrays: {"year": [2020, 2021], "sales": [100, 150]} 3. JSON string of format 1 or 2: '[{"year": 2020, "sales": 100}]' ALTERNATIVE (only for extremely large datasets where inline data is impractical): 4. File path to CSV or JSON: "/path/to/data.csv" or "/path/to/data.json" chart_type: Type of chart to create. Use list_chart_types to see all available types. Common types: bar, line, area, arrow, column, multiple_column, scatter, stacked_bar chart_config: Complete chart configuration as a Pydantic model dict

Returns: Chart ID and editor URL

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.

Publish a Datawrapper chart to make it publicly accessible. Returns the public URL of the published chart. IMPORTANT: Only use this tool when the user explicitly requests to publish the chart. Do not automatically publish charts after creation unless specifically asked.

Args: chart_id: ID of the chart to publish

Returns: Public URL of the published chart

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.

Get information about an existing Datawrapper chart, including its complete configuration, metadata, and URLs.

The returned configuration can be used to:

• Understand how a chart is styled and configured

• Adapt the configuration to a new dataset

• Clone a chart's styling to create similar visualizations

Returns:

• chart_id: The chart's unique identifier

• title: Chart title

• type: Simplified chart type name (bar, line, stacked_bar, etc.) - same format as used in list_chart_types and create_chart

• config: Complete Pydantic model configuration including all styling, colors, axes, tooltips, annotations, and other properties

• public_url: Public URL if published

• edit_url: Editor URL

Args: chart_id: ID of the chart to retrieve

Returns: Chart information including complete configuration and URLs

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.

Update an existing Datawrapper chart's data or configuration using Pydantic models.

⚠️ IMPORTANT LIMITATION: You CANNOT change the chart type with this tool. Chart types are immutable once created. To change from one chart type to another (e.g., column → stacked_bar, or line → area), you must create a new chart instead.

WHAT YOU CAN UPDATE: • Chart data (add/modify/replace data points) • Title, intro, byline, source information • Colors, styling, axes configuration • Tooltips, annotations, labels • Any other configuration options for the existing chart type

WHAT YOU CANNOT UPDATE: ✗ Chart type (bar, line, column, etc.) - this is permanent

The chart_config must use high-level Pydantic fields only (title, intro, byline, source_name, source_url, etc.). Do NOT use low-level serialized structures like 'metadata', 'visualize', or other internal API fields.

STYLING UPDATES: Use get_chart_schema to see available fields, then apply styling changes:

• Colors: {"color_category": {"sales": "#ff0000"}}

• Line properties: {"lines": [{"column": "sales", "width": "style2"}]}

• Axis settings: {"custom_range_y": [0, 200], "y_grid_format": "0,0"}

• Tooltips: {"tooltip_number_format": "0.0"}

See https://datawrapper.readthedocs.io/en/latest/ for detailed examples. The provided config will be validated through Pydantic and merged with the existing chart configuration.

Args: chart_id: ID of the chart to update data: New chart data (optional). Same formats as create_chart. chart_config: Updated chart configuration using high-level Pydantic fields (optional)

Returns: Confirmation message with editor URL

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.

Delete a Datawrapper chart permanently.

Args: chart_id: ID of the chart to delete

Returns: Confirmation message

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration.

Export a Datawrapper chart as PNG and display it inline. The chart must be created first using create_chart. Supports high-resolution output via the zoom parameter. IMPORTANT: Only use this tool when the user explicitly requests to see the chart image or export it as PNG. Do not automatically export charts after creation unless specifically asked.

Args: chart_id: ID of the chart to export width: Width of the image in pixels (optional) height: Height of the image in pixels (optional) plain: If true, exports only the visualization without header/footer zoom: Scale multiplier for resolution, e.g., 2 = 2x resolution transparent: If true, exports with transparent background border_width: Margin around visualization in pixels border_color: Color of the border, e.g., '#FFFFFF' (optional)

Returns: PNG image content