StacklokLabs/plotting-mcp

📊 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

make install

Using uv

uv sync

Usage

Running the Server

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

{
  "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
• CLI Quickstart Guide

Build the Docker image

docker build -t plotting-mcp .

Run with ToolHive

Run locally

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:

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.

kubectl apply -f toolhive-deployment.yaml

3. Once the MCP server is deployed, do port-forwarding

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

# 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