Datawrapper MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Datawrapper MCPCreate a line chart showing 2023 sales data and publish it"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that enables AI assistants to create Datawrapper charts. Built on the datawrapper Python library with Pydantic validation.
Example Usage
Here's a 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!Related MCP server: mcp-plots
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
Create a new API token
Add it to your MCP configuration as shown below
Installation
Claude Code
Using uvx (recommended)
Configure your MCP client in claude_desktop_config.json:
{
"mcpServers": {
"datawrapper": {
"command": "uvx",
"args": ["datawrapper-mcp"],
"env": {
"DATAWRAPPER_ACCESS_TOKEN": "your-token-here"
}
}
}
}Using pip
First install the package:
pip install datawrapper-mcpThen configure your MCP client in claude_desktop_config.json:
{
"mcpServers": {
"datawrapper": {
"command": "datawrapper-mcp",
"env": {
"DATAWRAPPER_ACCESS_TOKEN": "your-token-here"
}
}
}
}OpenAI Codex
CLI with uvx
Add this to ~/.codex/config.toml:
[mcp_servers.datawrapper]
args = ["datawrapper-mcp"]
command = "uvx"
startup_timeout_sec = 30
[mcp_servers.datawrapper.env]
DATAWRAPPER_ACCESS_TOKEN = "your-token-here"CLI with pip
First install the package:
pip install datawrapper-mcpThen add this to ~/.codex/config.toml:
[mcp_servers.datawrapper]
command = "datawrapper-mcp"
startup_timeout_sec = 30
[mcp_servers.datawrapper.env]
DATAWRAPPER_ACCESS_TOKEN = "your-token-here"Secure secrets
For enhanced security, you can configure a pass-through environment variable by ensuring that DATAWRAPPER_ACCESS_TOKEN is set in your environment, and replacing this in your config.toml:
[mcp_servers.datawrapper.env]
DATAWRAPPER_ACCESS_TOKEN = "your-token-here"With this:
env_vars = ["DATAWRAPPER_ACCESS_TOKEN"]This ensures that the value set for DATAWRAPPER_ACCESS_TOKEN in your environment is passed through to Codex without having to store the secret as text in a config file.
Desktop application
If you're using the Codex Desktop Application, you can set up the MCP in your settings under MCP servers:
Under Custom servers, click
Add serverUnder Name, enter
datawrapper-mcpSelect STDIO
Under Command to launch, type
uvx(you must have uv installed)Under Arguments, add
datawrapper-mcpUnder Environment variables, add
DATAWRAPPER_ACCESS_TOKENas the key and your token as the valueClick Save
Kubernetes Deployment
For enterprise deployments, this server can be deployed to Kubernetes using HTTP transport:
Building the Docker Image
docker build -t datawrapper-mcp:latest .Running with Docker
docker run -p 8501:8501 \
-e DATAWRAPPER_ACCESS_TOKEN=your-token-here \
-e MCP_SERVER_HOST=0.0.0.0 \
-e MCP_SERVER_PORT=8501 \
datawrapper-mcp:latestEnvironment Variables
DATAWRAPPER_ACCESS_TOKEN: Your Datawrapper API token (required)MCP_SERVER_HOST: Server host (default:0.0.0.0)MCP_SERVER_PORT: Server port (default:8501)MCP_SERVER_NAME: Server name (default:datawrapper-mcp)
Health Check Endpoint
The HTTP server includes a /healthz endpoint for Kubernetes liveness and readiness probes:
curl http://localhost:8501/healthz
# Returns: {"status": "healthy", "service": "datawrapper-mcp"}Kubernetes Configuration Example
apiVersion: apps/v1
kind: Deployment
metadata:
name: datawrapper-mcp
spec:
replicas: 1
selector:
matchLabels:
app: datawrapper-mcp
template:
metadata:
labels:
app: datawrapper-mcp
spec:
containers:
- name: datawrapper-mcp
image: datawrapper-mcp:latest
ports:
- containerPort: 8501
env:
- name: DATAWRAPPER_ACCESS_TOKEN
valueFrom:
secretKeyRef:
name: datawrapper-secrets
key: access-token
livenessProbe:
httpGet:
path: /healthz
port: 8501
initialDelaySeconds: 5
periodSeconds: 30
readinessProbe:
httpGet:
path: /healthz
port: 8501
initialDelaySeconds: 5
periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
name: datawrapper-mcp
spec:
selector:
app: datawrapper-mcp
ports:
- protocol: TCP
port: 8501
targetPort: 8501Maintenance
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