Plotly MCP Server - Implementation Plan

Project Overview

Goal: Graph Objects-only Plotly MCP server for Cursor
Output: HTML visualizations from natural language
Control: Minute-level customization of all chart elements
Scope: Complete coverage of 50+ Plotly trace types

Related MCP server: RapidChart MCP Server

Phase 1 Status ✅

Foundation Complete:

• ✅ MCP server with FastMCP framework (SDK 1.2.0+)

• ✅ Project structure for 49 trace types

• ✅ 5 basic trace builders (scatter, bar, line, pie, histogram)

• ✅ Figure assembly system

• ✅ Layout controllers (axes, styling)

• ✅ Sample data generation for testing

• ✅ Cursor integration ready

Quick Start

1. Install Dependencies

With UV (recommended):

# Install UV if you havent already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Setup project
cd /Users/arshad/Desktop/personal/code/plotly-mcp-claude
uv sync

With pip:

pip install -r requirements.txt

2. Run the MCP Server

cd src
python server.py

3. Add to Cursor MCP Settings

Add this to your Cursor MCP configuration:

{
  "mcpServers": {
    "plotly-mcp": {
      "command": "python",
      "args": ["/Users/arshad/Desktop/personal/code/plotly-mcp-claude/src/server.py"]
    }
  }
}

Available Tools (Phase 1)

Basic Trace Builders

1. create_scatter_plot - Scatter plots with markers, lines, or both

2. create_bar_chart - Vertical/horizontal bar charts with text labels

3. create_line_chart - Line charts with styling and fill options

4. create_pie_chart - Pie/donut charts with custom colors

5. create_histogram - Histograms with binning and normalization

Utility Tools

6. create_multi_trace_figure - Initialize figures for multiple traces

7. generate_sample_data - Create test data (linear, sine, random, categories)

Usage Examples

Scatter Plot

create_scatter_plot(
    x_data=[1, 2, 3, 4, 5],
    y_data=[2, 4, 1, 5, 3],
    colors="red",
    sizes=15,
    mode="markers+lines",
    name="My Data"
)

Bar Chart

create_bar_chart(
    x_data=["A", "B", "C", "D"],
    y_data=[20, 14, 23, 25],
    colors="blue",
    orientation="v",
    text=["20", "14", "23", "25"],
    name="Sales Data"
)

Pie Chart

create_pie_chart(
    labels=["Apple", "Orange", "Banana"],
    values=[30, 25, 45],
    hole=0.3,  # Donut chart
    textinfo="label+percent"
)

Generate Test Data

generate_sample_data(
    data_type="sine",
    size=100,
    noise=0.1
)

Architecture

plotly-mcp-claude/
├── src/
│   ├── server.py              # Main MCP server (FastMCP)
│   ├── traces/                # All trace builders (5/49 complete)
│   │   └── basic/            # Phase 1: scatter, bar, line, pie, histogram
│   ├── layouts/              # Layout controllers
│   │   ├── axes.py           # X/Y/Z axis configuration
│   │   └── styling.py        # Colors, fonts, margins, legends
│   ├── assembly/             # Figure building
│   │   └── builder.py        # Combine traces + layout
│   └── [themes/, nlp/]       # Future phases
├── data/                     # Sample datasets (future)
├── examples/                 # Usage examples (future)
├── tests/                    # Unit tests (future)
├── requirements.txt          # Python dependencies
├── pyproject.toml           # Project configuration
└── README.md                # This file

Complete Plotly Trace Types (Planned)

Phase 1 ✅ (5/49)

• Basic Charts: scatter ✅, bar ✅, line ✅, pie ✅, histogram ✅

Phase 2 📋 (15 more types)

• Statistical Charts: box, violin, heatmap, contour, splom, parcoords, parcats, histogram2d

• 3D Charts: scatter3d, surface, mesh3d, volume, isosurface, cone, streamtube

Phase 3 📋 (15 more types)

• Geographic Charts: choropleth, choroplethmap, choroplethmapbox, scattergeo, scattermap, scattermapbox, densitymap, densitymapbox

• Financial Charts: candlestick, ohlc, waterfall

• Hierarchical Charts: treemap, sunburst, icicle, sankey

Phase 4 📋 (14 more types)

• Polar & Coordinates: scatterpolar, scatterpolargl, scattersmith, scatterternary, carpet, scattercarpet

• Specialized Charts: funnel, funnelarea, indicator, image, table

• Additional: barpolar, histogram2dcontour, contourcarpet

Total: 49 trace builders planned

Implementation Timeline

• Phase 1 ✅ Foundation (5 basic traces) - COMPLETE

• Phase 2 🚧 Statistical & 3D traces (15 more types)

• Phase 3 📋 Geographic & Financial traces (15 more types)

• Phase 4 📋 Remaining traces & complete layout (14 more types)

• Phase 5 📋 Theming system (sci-fi, corporate, dark themes)

• Phase 6 📋 Natural language interface

Technical Details

MCP Server Features

• Built with MCP Python SDK 1.2.0+

• FastMCP framework for easy tool definition

• Async/await pattern throughout

• Type hints for all parameters

• Error handling with detailed messages

• Logging for debugging

Chart Features

• HTML output with embedded Plotly.js

• Interactive charts (zoom, pan, hover)

• Responsive design (800x600 default)

• Professional styling (plotly_white theme)

• Full customization of all visual elements

Testing

Each trace type returns complete HTML that can be:

• Viewed directly in browser

• Embedded in applications

• Displayed in Cursor/Claude interface

Next Steps for Phase 2

Ready to add Statistical & 3D traces:

• Box plots - quartile visualization

• Violin plots - distribution shape

• Heatmaps - 2D data correlation

• 3D scatter - three-dimensional points

• Surface plots - 3D mathematical functions

Contributing

The modular architecture makes it easy to add new trace types:

1. Create trace builder in src/traces/{category}/

2. Add tool decorator in src/server.py

3. Test with sample data

4. Update documentation

Requirements

• Python 3.10+

• MCP Python SDK 1.2.0+

• Plotly 5.0+

• Modern browser for viewing charts

Phase 1 Complete - 5/49 trace types implemented Ready for Phase 2: Statistical & 3D visualization

Built with ❤️ for the Claude ecosystem