https://github.com/stackloklabs/plotting-mcp
MCP server that transforms CSV data into visualizations
https://github.com/stackloklabs/plotting-mcp
cartopy maps matplotlib mcp mcp-server plotting python seaborn
Last synced: about 2 months ago
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MCP server that transforms CSV data into visualizations
- Host: GitHub
- URL: https://github.com/stackloklabs/plotting-mcp
- Owner: StacklokLabs
- License: apache-2.0
- Created: 2025-07-31T09:01:22.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2026-03-11T10:52:30.000Z (3 months ago)
- Last Synced: 2026-03-11T17:19:27.424Z (3 months ago)
- Topics: cartopy, maps, matplotlib, mcp, mcp-server, plotting, python, seaborn
- Language: Python
- Homepage:
- Size: 144 KB
- Stars: 7
- Watchers: 1
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
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README
# 📊 Plotting MCP Server
A MCP (Model Context Protocol) server that transforms CSV data into beautiful visualizations. Built with Python and optimized for seamless integration with AI assistants and chat applications.
## ✨ Features
- **📈 Multiple Plot Types**: Create line charts, bar graphs, pie charts, and world maps
- **🌍 Geographic Visualization**: Built-in support for plotting coordinate data on world maps using Cartopy
- **🔧 Flexible Parameters**: Fine-tune your plots with JSON-based configuration options
- **📱 Chat-Ready Output**: Returns base64-encoded PNG images perfect for AI chat interfaces
- **⚡ Fast Processing**: Efficient CSV parsing and plot generation with pandas and matplotlib
## Installation
### Using Makefile
```bash
make install
```
### Using uv
```bash
uv sync
```
## Usage
### Running the Server
```bash
uv run plotting-mcp
```
The server runs on port 9090 by default.
### Tools
#### `generate_plot`
Transform your CSV data into stunning visualizations.
**Parameters:**
- `csv_data` (str): CSV data as a string
- `plot_type` (str): Plot type - `line`, `bar`, `pie`, or `worldmap`
- `json_kwargs` (str): JSON string with plotting parameters for customization
**Plotting Options:**
- **Line/Bar Charts**: Use Seaborn parameters (`x`, `y`, `hue` for data mapping)
- **World Maps**: Automatic coordinate detection (`lat`/`latitude`/`y` and `lon`/`longitude`/`x`)
- Customize with `s` (size), `c` (color), `alpha` (transparency), `marker` (style)
- **Pie Charts**: Supports single column (value counts) or two columns (labels + values)
**Returns:** Base64-encoded PNG image ready for display
## 🤖 AI Assistant Integration
Perfect for enhancing AI conversations with data visualization capabilities. The server returns plots as base64-encoded PNG images that display seamlessly in:
- **LibreChat**: Direct integration for chat-based data analysis
- **Claude Desktop**: Through `mcp-remote` command to transform from HTTP transport to stdio
```json
{
"mcpServers": {
"plotting": {
"command": "uvx",
"args": [
"--from", "/path/to/plotting-mcp",
"plotting-mcp", "--transport=stdio"
]
}
}
}
```
- **Custom AI Applications**: Easy integration via MCP protocol
- **Development Tools**: Compatible with any MCP-enabled environment
**Image Format**: High-quality PNG with configurable DPI and sizing
## 🚀 ToolHive Deployment
Deploy and manage your plotting server effortlessly with ToolHive - a platform that provides containerized, secure environments for MCP servers across UI, CLI, and Kubernetes modes.
**Benefits:**
- 🔒 **Secure Containerization**: Isolated environments with comprehensive security controls
- ⚙️ **Multiple Deployment Options**: UI, CLI, and Kubernetes support
- 🔧 **Developer-Friendly**: Seamless integration with popular development tools
📚 **Resources:**
- [ToolHive Documentation](https://docs.stacklok.com/toolhive/)
- [CLI Quickstart Guide](https://docs.stacklok.com/toolhive/tutorials/quickstart-cli)
### Build the Docker image
```bash
docker build -t plotting-mcp .
```
### Run with ToolHive
#### Run locally
```bash
thv run --name plotting-mcp --transport streamable-http plotting-mcp:latest
```
#### Run with ToolHive in K8s with ToolHive operator
1. Create a PVC for the MCP server. This is needed since the plotting libraries Matplotlib and Cartopy require a writable filesystem to cache data:
```bash
kubectl apply -f toolhive-pvc.yaml
```
2. Deploy the MCP server in K8s. In the `toolhive-deployment.yaml`, you can customize the `image` field to point to your image registry.
```bash
kubectl apply -f toolhive-deployment.yaml
```
3. Once the MCP server is deployed, do port-forwarding
```bash
kubectl port-forward svc/mcp-plotting-mcp-proxy 9090:9090
```
## 🛠️ Development
Built with modern Python tooling for a great developer experience.
**Tech Stack:**
- 🐍 **Python 3.13+**: Latest Python features
- 📊 **Seaborn & Matplotlib**: Professional-grade plotting
- 🌍 **Cartopy**: Advanced geospatial visualization
- ⚡ **FastMCP**: High-performance MCP server framework
- 🔧 **UV**: Fast Python package management
### Code Quality
```bash
# Format code and fix linting issues
make format
# Type checking
make typecheck
# Or use uv directly
uv run ruff format .
uv run ruff check --fix .
uv run ty check
```