📊 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

Using uv

Usage

Running the Server

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

• 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

Run with ToolHive

Run locally

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:

2. Deploy the MCP server in K8s. In the toolhive-deployment.yaml, you can customize the image field to point to your image registry.

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

🛠️ 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