Datawrapper

平台与服务

by palewire

基于 Model Context Protocol 的 MCP 服务器,可借助 AI 助手快速创建 Datawrapper 图表。

什么是 Datawrapper

基于 Model Context Protocol 的 MCP 服务器,可借助 AI 助手快速创建 Datawrapper 图表。

README

PyPI MCP Registry Docker Hub

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

<!-- mcp-name: io.github.palewire/datawrapper-mcp -->

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:

code
"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

ToolDescription
list_chart_typesList available chart types with descriptions
get_chart_schemaGet the full configuration schema for a chart type
create_chartCreate a new chart with data and configuration
update_chartUpdate an existing chart's data or styling
publish_chartPublish a chart to make it publicly accessible
get_chartRetrieve a chart's configuration and metadata
delete_chartPermanently delete a chart
export_chart_pngExport 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)

json
{
  "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:

code
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

ClientConfig fileTransport
Claude Desktopclaude_desktop_config.jsonstdio or streamable-http
Claude.aiManaged connectorstreamable-http
Claude Code.claude/settings.jsonstdio
VS Code Copilot.vscode/mcp.jsonstdio
Cursor.cursor/mcp.jsonstdio or streamable-http
ChatGPTDev Mode settingsstreamable-http only
OpenAI Codex~/.codex/config.tomlstdio

常见问题

Datawrapper 是什么?

基于 Model Context Protocol 的 MCP 服务器,可借助 AI 助手快速创建 Datawrapper 图表。

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