# Datawrapper [Health: Active] **Category:** 📊 Data Visualization **Repository:** https://github.com/palewire/datawrapper-mcp **GitHub Stars:** 60 **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/datawrapper ## Description A Model Context Protocol (MCP) server for creating Datawrapper charts using AI assistants ## Tools Capabilities this server exposes over MCP: - **list_chart_types** — ⚠️ 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 - **get_chart_schema** — ⚠️ 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. - **check_datawrapper_connection** — ⚠️ 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. - **create_chart** — ⚠️ 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. - **publish_chart** — ⚠️ 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. - **get_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 - **update_chart** — ⚠️ 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. - **delete_chart** — ⚠️ DATAWRAPPER MCP TOOL ⚠️ This is part of the Datawrapper MCP server integration. --- Delete a Datawrapper chart permanently. - **export_chart_png** — ⚠️ 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. ## Claude Desktop Quick Installation Install path detected from listing signals. Uses `uvx` (confidence: high): ```json "mcpServers": { "datawrapper": { "command": "uvx", "args": ["datawrapper-mcp"] } } ``` ## Documentation & README [![PyPI](https://img.shields.io/pypi/v/datawrapper-mcp)](https://pypi.org/project/datawrapper-mcp/) [![MCP Registry](https://img.shields.io/badge/MCP-Registry-blue)](https://registry.modelcontextprotocol.io/?q=datawrapper) [![Docker Hub](https://img.shields.io/docker/v/palewire/datawrapper-mcp?label=Docker%20Hub)](https://hub.docker.com/r/palewire/datawrapper-mcp) A Model Context Protocol (MCP) server and app for creating Datawrapper charts using AI assistants. Built on the [datawrapper Python library](https://github.com/chekos/datawrapper). > **Status: early and experimental.** This project — including its Claude > Code/Desktop plugin and marketplace listing — is under active development, > and behavior can change between releases. In particular, the > plugin/marketplace install path has known gaps (see > [issue #78](https://github.com/palewire/datawrapper-mcp/issues/78)) — don't > treat it yet as a finished, zero-maintenance install path for a wider > rollout. ## Example Usage You can provide a data file and simply ask for the chart you want. The draft will soon appear in the panel. ![Books chat chart](https://raw.githubusercontent.com/palewire/datawrapper-mcp/HEAD/.github/books.png) Here's a more complete example showing how to create, publish, update, and display a chart by chatting with the assistant: ``` "Create a datawrapper line chart showing temperature trends with this data: 2020, 15.5 2021, 16.0 2022, 16.5 2023, 17.0" # The assistant creates the chart and returns the chart ID, e.g., "abc123" "Publish it." # The assistant publishes it and returns the public URL "Update chart with new data for 2024: 17.2°C" # The assistant updates the chart with the new data point "Make the line color dodger blue." # The assistant updates the chart configuration to set the line color "Show me the editor URL." # The assistant returns the Datawrapper editor URL where you can view/edit the chart "Show me the PNG." # The assistant embeds the PNG image of the chart in its contained response. "Suggest five ways to improve the chart." # See what happens! ``` ## Tools | Tool | Description | | ------------------ | -------------------------------------------------- | | `list_chart_types` | List available chart types with descriptions | | `get_chart_schema` | Get the full configuration schema for a chart type | | `create_chart` | Create a new chart with data and configuration | | `update_chart` | Update an existing chart's data or styling | | `publish_chart` | Publish a chart to make it publicly accessible | | `get_chart` | Retrieve a chart's configuration and metadata | | `delete_chart` | Permanently delete a chart | | `export_chart_png` | Export a chart as a PNG image | ## Chart Types bar, line, area, arrow, column, multiple column, scatter, stacked bar Use `list_chart_types` to see descriptions, then `get_chart_schema` to explore configuration options for any type. ## Getting Started ### Requirements - A Datawrapper account (sign up at https://datawrapper.de/signup/) - An MCP client such as [Claude](https://claude.ai/) or [OpenAI Codex](https://openai.com/codex/) - Python 3.10 or higher ### Get Your API Token 1. Go to https://app.datawrapper.de/account/api-tokens 2. Create a new API token 3. Add it to your MCP configuration as shown in the [installation guide](https://github.com/palewire/datawrapper-mcp/blob/HEAD/INSTALLATION.md) ### Quick Start (Claude Code) ```json { "mcpServers": { "datawrapper": { "command": "uvx", "args": ["datawrapper-mcp"], "env": { "DATAWRAPPER_ACCESS_TOKEN": "your-token-here" } } } } ``` For other clients (Claude Desktop, Claude.ai, Cursor, VS Code Copilot, ChatGPT, OpenAI Codex, OpenClaw) and Kubernetes deployment, see the [installation guide](https://github.com/palewire/datawrapper-mcp/blob/HEAD/INSTALLATION.md). > **Installing via a plugin marketplace/directory (Claude Desktop's plugin > browser, ClawHub, etc.) is still experimental.** These interactive install > flows currently have no working way to collect required environment > variables like `DATAWRAPPER_ACCESS_TOKEN` — see > [issue #78](https://github.com/palewire/datawrapper-mcp/issues/78) for > details. The manually-edited config shown above (and throughout the > [installation guide](https://github.com/palewire/datawrapper-mcp/blob/HEAD/INSTALLATION.md)) is the reliable path today. ### Using Your Own Token (Hosted Deployments) When connecting to a hosted instance of the server over HTTP, you can authenticate with your own Datawrapper API token by sending it in the `Authorization` header: ``` Authorization: Bearer ``` This ensures charts are created under your account instead of the server operator's. The token is read from the header automatically — no need to include it in every tool call. You can also pass `access_token` directly as a tool argument, which takes precedence over the header. When neither is provided, the server falls back to its `DATAWRAPPER_ACCESS_TOKEN` environment variable. ### Custom Instructions Set `DATAWRAPPER_MCP_INSTRUCTIONS` to have the server hand your own free-text guidance to connecting MCP clients (many, including Claude, fold this into the model's context). Use it for house style rules — required fields, naming conventions, and the like — without forking the server: ```json "env": { "DATAWRAPPER_ACCESS_TOKEN": "your-token-here", "DATAWRAPPER_MCP_INSTRUCTIONS": "Every chart needs alt text and a CMS slug in its notes field." } ``` ### Supported Clients | Client | Config file | Transport | | --------------- | ---------------------------- | ------------------------ | | Claude Desktop | `claude_desktop_config.json` | stdio or streamable-http | | Claude.ai | Personal or org connector | streamable-http | | Claude Code | [Plugin marketplace](https://github.com/palewire/datawrapper-mcp) or `.mcp.json` | stdio | | VS Code Copilot | `.vscode/mcp.json` | stdio | | Cursor | `.cursor/mcp.json` | stdio or streamable-http | | ChatGPT | Dev Mode settings | streamable-http only | | OpenAI Codex | `~/.codex/config.toml` | stdio | | OpenClaw | [ClawHub plugin](https://clawhub.ai/palewire/plugins/datawrapper-mcp) or `openclaw.json` | stdio |