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palewire/datawrapper-mcp

palewire/datawrapper-mcp

Create Datawrapper charts with AI
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收录自 abordage/awesome-mcp 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
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收录于 2026-10-14 · 最近见到 2026-10-16 · 共见到 4 次 · 被 3 个来源列出
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综合分 303 · 3 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
我们收录于 2026-10-14;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

A Model Context Protocol (MCP) server and app for creating Datawrapper charts us

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

> **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:
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 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)
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:
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
来源
awesome · https://github.com/palewire/datawrapper-mcp
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