A Model Context Protocol (MCP) server that enables AI assistants to create Datawrapper charts. Built on the [datawrapper Python library](https://github.com/chekos/datawrapper) 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](https://claude.ai/) or [OpenAI Codex](https://openai.com/codex/)
- Python 3.10 or higher
- A Python package installer such as [pip](https://pip.pypa.io/en/stable/installation/) or [uvx](https://docs.astral.sh/uv/getting-started/installation/)

### 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`:

```json
{
  "mcpServers": {
    "datawrapper": {
      "command": "uvx",
      "args": ["datawrapper-mcp"],
      "env": {
        "DATAWRAPPER_ACCESS_TOKEN": "your-token-here"
      }
    }
  }
}
```

**Using pip**

First install the package:

```bash
pip install datawrapper-mcp
```

Then configure your MCP client in `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "datawrapper": {
      "command": "datawrapper-mcp",
      "env": {
        "DATAWRAPPER_ACCESS_TOKEN": "your-token-here"
      }
    }
  }
}
```

#### OpenAI Codex

**CLI with uvx**

Add this to `~/.codex/config.toml`:

```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:

```bash
pip install datawrapper-mcp
```

Then add this to `~/.codex/config.toml`:

```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`:

```toml
[mcp_servers.datawrapper.env]
DATAWRAPPER_ACCESS_TOKEN = "your-token-here"
```

With this:

```toml
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](https://openai.com/codex/), 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](https://docs.astral.sh/uv/getting-started/installation/))
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

```bash
docker build -t datawrapper-mcp:latest .
```

#### Running with Docker

```bash
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:

```bash
curl http://localhost:8501/healthz
# Returns: {"status": "healthy", "service": "datawrapper-mcp"}
```

#### Kubernetes Configuration Example

```yaml
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
```