Datawrapper

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.

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

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