A Model Context Protocol (MCP) server that enables AI assistants to create Datawrapper charts. Built on the datawrapper Python library with Pydantic validation.

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

# 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!

# 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
• A Python package installer such as pip or uvx

# # 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 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-mcp

Then 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-mcp

Then 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:

1. Under Custom servers, click Add server
2. Under Name, enter datawrapper-mcp
3. Select STDIO
4. Under Command to launch, type uvx (you must have uv installed)
5. Under Arguments, add datawrapper-mcp
6. Under Environment variables, add DATAWRAPPER_ACCESS_TOKEN as the key and your token as the value
7. Click 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:latest

# # # Environment 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: 8501