Plots converts tabular data into professional charts and graphs with minimal configuration. It supports multiple visualization types including bar charts, line graphs, pie charts, scatter plots, and heatmaps, rendering them as Mermaid diagrams, PNG images, or base64-encoded text for direct embedding in documents and chat interfaces. Key features: - Multiple chart types: bar, line, pie, scatter, and heatmap visualizations - Flexible output formats: Mermaid (default, renders in Cursor/Claude Desktop), PNG images, or base64 text - Natural data input through simple prompts or structured tabular data - Customizable themes and layouts with smart field mapping suggestions - Easy deployment via PyPI, uvx (zero-install), or Docker - Optimized workflow with guided prompts to refine visualizations

Plots MCP Server

[](https://pypi.org/project/mcp-plots/) [](https://pepy.tech/projects/mcp-plots) [](https://smithery.ai/server/@MR901/mcp-plots) [](https://glama.ai/mcp/servers/@MR901/mcp-plots) [](https://pypi.org/project/mcp-plots/) [](LICENSE)

A Model Context Protocol (MCP) server for data visualization. It exposes tools to render charts (line, bar, pie, scatter, heatmap, etc.) from data and returns the plot as image/base64 text/mermaid diagram.

Why MCP Plots?

Quick Usage

Quick Start

PyPI Installation (Recommended)

pip install mcp-plots
mcp-plots  # Start the server

For Cursor Users

   {
     "mcpServers": {
       "plots": {
         "command": "mcp-plots",
         "args": ["--transport", "stdio"]
       }
     }
   }
   

   {
     "mcpServers": {
       "plots": {
         "command": "uvx",
         "args": ["mcp-plots", "--transport", "stdio"]
       }
     }
   }
   

Development Installation

uvx --from git+https://github.com/mr901/mcp-plots.git run-server.py

Documentation → | Quick Start → | API Reference →

MCP Registry

This server is published under the MCP registry identifier io.github.MR901/mcp-plots. You can discover/verify it via the official registry API:

curl "https://registry.modelcontextprotocol.io/v0/servers?search=io.github.MR901/mcp-plots"

Registry metadata for this project is tracked in server.json.

Install with Smithery

This repository includes a smithery.yaml for easy setup with Smithery.

Example install using the Smithery CLI (adjust --client as needed, e.g. cursor, claude):

npx -y @smithery/cli install \
  https://raw.githubusercontent.com/mr901/mcp-plots/main/smithery.yaml \
  --client cursor

After installation, your MCP client should be able to start the server over stdio using the command defined in smithery.yaml.

Project layout

src/
  app/                # Server construction and runtime
    server.py
  capabilities/       # MCP tools and prompts
    tools.py
    prompts.py
  visualization/      # Plotting engines and configurations
    chart_config.py
    generator.py

Requirements

Setup Routes

uvx (Recommended)

The easiest way to run the MCP server without managing Python environments:

# Run directly with uvx (no installation needed)
uvx --from git+https://github.com/mr901/mcp-plots.git run-server.py
Or install and run the command
uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots
With custom options
uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots --port 8080 --log-level DEBUG

Why uvx?

PyPI (Traditional Installation)

1) Install dependencies

pip install -r requirements.txt

2) Run the server (HTTP transport, default port 8000)

python -m src --transport streamable-http --host 0.0.0.0 --port 8000 --log-level INFO

3) Run with stdio (for MCP clients that spawn processes)

python -m src --transport stdio

Local Development (from source)