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palewire

datawrapper-mcp

by palewire

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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.

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, 15.5
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

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, Cursor, VS Code Copilot, ChatGPT, OpenAI Codex) and Kubernetes deployment, see the installation guide.

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.

Supported Clients

Client

Config file

Transport

Claude Desktop

claude_desktop_config.json

stdio or streamable-http

Claude.ai

Managed connector

streamable-http

Claude Code

.claude/settings.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

Install Server
A
license - permissive license
A
quality
C
maintenance

Maintenance

Maintainers
Response time
6dRelease cycle
24Releases (12mo)
Issues opened vs closed

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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