Datawrapper

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A Model Context Protocol (MCP) server and app for creating Datawrapper charts using AI assistants. Built on the datawrapper Python library.

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) — 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]

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, 15.52021, 16.02022, 16.52023, 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

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 or OpenAI 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

Quick Start (Claude Code)

{  "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.

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 for details. The manually-edited config shown above (and throughout the installation guide) 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 <your-datawrapper-api-token>

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:

"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