Datawrapper

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration. --- Check which Datawrapper account is currently authenticated, without creating, modifying, or deleting anything. Use this to troubleshoot access issues - especially when connected through a personal Claude connector using a per-user Authorization header, where a misconfigured header silently falls back to a different account instead of raising an error. If the reported email isn't the one you expected, the header isn't reaching the server correctly.

⚠️ 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 rang…

⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration. --- Delete a Datawrapper chart permanently.

⚠️ 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.

⚠️ 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

⚠️ 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.

⚠️ 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. --- Publish a Datawrapper chart to make it publicly accessible and render a preview in chat. IMPORTANT: Only use this tool when the user explicitly requests to publish the chart. Do not automatically publish charts after creation unless specifically asked.

⚠️ 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.