Plotnine MCP Server
A Model Context Protocol (MCP) server that brings ggplot2's grammar of graphics to Python through plotnine, enabling AI-powered data visualization via natural language.
Create publication-quality statistical graphics through chat using plotnine's Python implementation of R's beloved ggplot2. This modular MCP server allows Claude and other AI assistants to generate highly customizable visualizations by composing layers through the grammar of graphics paradigm.
Features
Core Visualization
- 🎨 Multi-Layer Plots: Combine multiple geometries in a single plot (scatter + trend lines, boxplots + jitter, etc.)
- Grammar of Graphics: Compose plots using aesthetics, geometries, scales, themes, facets, and coordinates
- 20+ Geometry Types: Points, lines, bars, histograms, boxplots, violins, and more
- Multiple Data Sources: Load data from files (CSV, JSON, Parquet, Excel), URLs, or inline JSON
- Multiple Output Formats: PNG, PDF, SVG with configurable dimensions and DPI
Smart Features (NEW!)
- 📋 9 Plot Templates: Pre-configured templates for common patterns (time series, scatter with trend, distribution comparison, etc.)
- 🤖 AI Template Suggestions: Analyzes your data and recommends appropriate plot types
- 🎨 21 Color Palettes: Colorblind-safe, scientific, categorical, corporate, sequential, and diverging palettes
- 📊 Data Preview: Inspect data before plotting with comprehensive summaries
- 🎯 Smart Error Messages: Fuzzy matching suggests corrections for typos in column names, geom types, and themes
- 💾 Config Export/Import: Save and reuse plot configurations as JSON files
Data Manipulation (NEW!)
- 🔄 12 Data Transformations: filter, group_summarize, sort, select, rename, mutate, drop_na, fill_na, sample, unique, rolling, pivot
- ⚡ Batch Processing: Create multiple plots in one operation
- 🔗 Chained Transforms: Apply multiple transformations in sequence
Theming & Customization
- Flexible Theming: Built-in themes with extensive customization options
- Statistical Transformations: Add smoothing, binning, density estimation, and summaries
- Faceting: Split plots by categorical variables using wrap or grid layouts
Installation
1. Clone or download this repository
cd plotnine-mcp
2. Install dependencies
Using pip:
pip install -e .
For full functionality (parquet and Excel support):
pip install -e ".[full]"
3. Configure Your MCP Client
Finding Your Installation Path
First, find where the plotnine-mcp command was installed:
which plotnine-mcp
This will show something like /path/to/python/bin/plotnine-mcp. Use this full path in the configurations below.
Claude Desktop
Add the server to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Recommended (using entry point):
{
"mcpServers": {
"plotnine": {
"command": "/path/to/your/python/bin/plotnine-mcp",
"args": []
}
}
}
Alternative (using python -m):
{
"mcpServers": {
"plotnine": {
"command": "python",
"args": ["-m", "plotnine_mcp.server"]
}
}
}
If you installed in a virtual environment, replace with the full path:
{
"mcpServers": {
"plotnine": {
"command": "/path/to/venv/bin/plotnine-mcp",
"args": []
}
}
}
Cursor
Recommended approach: Configure via .cursor/mcp.json in your project:
{
"mcpServers": {
"plotnine": {
"command": "/path/to/your/python/bin/plotnine-mcp",
"args": []
}
}
}
Alternative: Add to Cursor global settings by opening the command palette (Cmd/Ctrl+Shift+P) and searching for "Preferences: Open User Settings (JSON)":
{
"mcp.servers": {
"plotnine": {
"command": "/path/to/your/python/bin/plotnine-mcp",
"args": []
}
}
}
Using python -m alternative:
{
"mcpServers": {
"plotnine": {
"command": "python",
"args": ["-m", "plotnine_mcp.server"]
}
}
}
VSCode (with Cline/Roo-Cline)
Add to your VSCode MCP settings file:
macOS/Linux: ~/.config/Code/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json
Windows: %APPDATA%\Code\User\globalStorage\rooveterinaryinc.roo-cline\settings\cline_mcp_settings.json
{
"mcpServers": {
"plotnine": {
"command": "/path/to/your/python/bin/plotnine-mcp",
"args": []
}
}
}
For other MCP clients in VSCode, consult their specific documentation for MCP server configuration.
4. Restart Your Application
Restart Claude Desktop, Cursor, or VSCode for the changes to take effect. The plotnine MCP server should now be available!
Usage
Basic Example
Create a scatter plot from data.csv with x=age and y=height
Advanced Example
Create a line plot from sales_data.csv showing:
- x: date, y: revenue, color by region
- Use a minimal theme with figure size 12x6
- Add a smooth trend line
- Facet by product category
- Label the plot "Q4 Sales Performance"
- Save as PDF
Available Tools (11 Total)
Core Tools
create_plot
Create a plotnine visualization with full customization.
Required Parameters:
data_source: Data source configurationaes: Aesthetic mappings (column names)geomorgeoms: Geometry specification(s)
Optional Parameters:
scales: Array of scale configurationstheme: Theme configurationfacets: Faceting configurationlabels: Plot labels (title, x, y, caption, subtitle)coords: Coordinate system configurationstats: Statistical transformationstransforms: Data transformations (NEW!)output: Output configuration (format, size, DPI, directory)
list_geom_types
List all 20+ available geometry types with descriptions.
Data Tools (NEW!)
preview_data
Preview and inspect data before creating plots. Returns dataset shape, column types, first rows, statistics, and missing values.
Parameters:
data_source: Data source configurationrows: Number of rows to preview (default: 5)
Template Tools (NEW!)
list_plot_templates
List all 9 available plot templates with descriptions:
- time_series
- scatter_with_trend
- distribution_comparison
- category_breakdown
- correlation_heatmap
- boxplot_comparison
- multi_line
- histogram_with_density
- before_after
create_plot_from_template
Create a plot using a predefined template. Just provide data and aesthetics; the template handles the rest.
Parameters:
template_name: Name of the templatedata_source: Data source configurationaes: Aesthetic mappingslabels: Optional labelsoutput: Optional output configoverrides: Optional overrides for template settings
suggest_plot_templates
Analyze your data and get AI-powered plot recommendations based on column types and optional goal.
Parameters:
data_source: Data source to analyzegoal: Optional goal (e.g., "compare distributions", "show trend")
Style Tools (NEW!)
list_themes
List all available themes for plot styling with descriptions and customization options.
list_color_palettes
List 21 color palettes across 6 categories:
- Colorblind-safe (3 palettes)
- Scientific (4 palettes)
- Categorical (4 palettes)
- Corporate (3 palettes)
- Sequential (4 palettes)
- Diverging (3 palettes)
Parameters:
category: Optional category filter
Configuration Tools
export_plot_config
Export plot configuration to JSON for reuse and sharing.
Parameters:
config: The plot configuration to exportfilename: Output filenamedirectory: Output directory (default: './plot_configs')
import_plot_config
Import and use a saved plot configuration with optional overrides.
Parameters:
config_path: Path to saved configurationoverrides: Optional parameter overrides
Batch Tools (NEW!)
batch_create_plots
Create multiple plots in one operation. Perfect for generating plots for all columns, pairwise comparisons, or different visualizations of the same data.
Parameters:
plots: Array of plot configurations
Geometry Types
- point: Scatter plot points
- line: Line plot connecting points
- bar: Bar chart (counts by default)
- col: Column chart (identity stat)
- histogram: Histogram of continuous data
- boxplot: Box and whisker plot
- violin: Violin plot for distributions
- area: Filled area under line
- density: Kernel density plot
- smooth: Smoothed conditional means
- jitter: Jittered points (reduces overplotting)
- tile: Heatmap/tile plot
- text: Text annotations
- errorbar: Error bars
- hline/vline/abline: Reference lines
- path: Path connecting points in order
- polygon: Filled polygon
- ribbon: Ribbon for intervals
Examples
Simple Scatter Plot
{
"data_source": {
"type": "file",
"path": "./data/iris.csv"
},
"aes": {
"x": "sepal_length",
"y": "sepal_width",
"color": "species"
},
"geom": {
"type": "point",
"params": {"size": 3, "alpha": 0.7}
}
}
Line Plot with Theme
{
"data_source": {
"type": "url",
"path": "https://example.com/timeseries.csv"
},
"aes": {
"x": "date",
"y": "value",
"color": "category"
},
"geom": {
"type": "line",
"params": {"size": 1.5}
},
"scales": [
{
"aesthetic": "x",
"type": "datetime",
"params": {"date_breaks": "1 month"}
}
],
"theme": {
"base": "minimal",
"customizations": {
"figure_size": [12, 6],
"legend_position": "bottom"
}
},
"labels": {
"title": "Time Series Analysis",
"x": "Date",
"y": "Value"
}
}
Faceted Boxplot
{
"data_source": {
"type": "inline",
"data": [
{"group": "A", "category": "X", "value": 10},
{"group": "A", "category": "Y", "value": 15},
{"group": "B", "category": "X", "value": 12}
]
},
"aes": {
"x": "group",
"y": "value",
"fill": "group"
},
"geom": {
"type": "boxplot"
},
"facets": {
"type": "wrap",
"facets": "~ category"
},
"theme": {
"base": "bw"
}
}
Multi-Layer Plot: Scatter + Smooth Trend
NEW! Layer multiple geometries to create complex visualizations:
{
"data_source": {
"type": "file",
"path": "./data/measurements.csv"
},
"aes": {
"x": "time",
"y": "value",
"color": "sensor"
},
"geoms": [
{
"type": "point",
"params": {"size": 2, "alpha": 0.6}
},
{
"type": "smooth",
"params": {"method": "lm", "se": false}
}
],
"theme": {
"base": "minimal",
"customizations": {"figure_size": [12, 6]}
},
"labels": {
"title": "Sensor Readings with Trend Lines",
"x": "Time",
"y": "Measurement"
}
}
Boxplot with Jittered Points
Show both distribution summary and individual data points:
{
"data_source": {
"type": "file",
"path": "./data/experiment.csv"
},
"aes": {
"x": "treatment",
"y": "response",
"fill": "treatment"
},
"geoms": [
{
"type": "boxplot",
"params": {"alpha": 0.7}
},
{
"type": "jitter",
"params": {"width": 0.2, "alpha": 0.5, "size": 1}
}
],
"theme": {
"base": "bw"
},
"labels": {
"title": "Treatment Effects with Individual Observations"
}
}
Chat Examples
You can create plots through natural language:
"Create a histogram of the 'age' column from users.csv"
"Make a scatter plot with smooth trend line showing price vs size, colored by category"
"Plot a line chart from sales.csv with date on x-axis and revenue on y-axis, faceted by region, using a dark theme"
"Create a violin plot comparing distributions of test scores across different schools"
"Make a boxplot with individual points overlaid showing temperature by season"
"Create a scatter plot with a linear trend line for each category, showing the relationship between hours studied and test scores"
Using New Tools
"Preview the data from sales.csv before plotting"
"What themes are available?"
"Show me all available plot templates"
"Suggest appropriate plot types for my data"
"Create a time series plot using the template"
"List color palettes in the scientific category"
"Export this plot configuration so I can reuse it later"
"Load the plot config from my_config.json and use it with a different dataset"
"Create a plot from the saved configuration but change the theme to minimal"
"Create plots for each category in my dataset" (batch processing)
"Filter the data to show only active users, then create a histogram" (data transformations)
New Examples
Using Templates
Create a scatter plot with trend line using a template:
"Use the scatter_with_trend template to plot height vs weight from my data"
This automatically creates a plot with:
- Scatter points (with transparency)
- Linear regression line
- Confidence interval
- Minimal theme
Using Color Palettes
"Create a bar chart colored using the colorblind-safe Okabe-Ito palette"
Data Transformations
"Filter sales data to show only Q4, group by region, sum the revenue, and create a bar chart"
This applies transformations before plotting:
- Filter:
"quarter == 'Q4'" - Group & summarize: by region, sum revenue
- Plot: bar chart of results
Batch Processing
"Create histogram plots for all numeric columns in my dataset"
Configuration Options
Themes
Available base themes:
gray(default)bw(black and white)minimalclassicdarklightvoid
Scale Types
- Positional: continuous, discrete, log10, sqrt, datetime
- Color/Fill: gradient, discrete, brewer
Coordinate Systems
cartesian(default)flip(swap x and y)fixed(fixed aspect ratio)trans(transformed coordinates)
Output
By default, plots are saved to ./output directory as PNG files with 300 DPI. You can customize:
- format: png, pdf, svg
- filename: Custom filename (auto-generated by default)
- width/height: Dimensions in inches
- dpi: Resolution for raster formats
- directory: Output directory path
Troubleshooting
"Module not found" errors
Ensure you've installed the package:
pip install -e .
Parquet/Excel support
Install optional dependencies:
pip install -e ".[full]"
"Cannot find data file"
Use absolute paths or paths relative to where Claude Desktop is running.
Plot not rendering
Check that:
- Column names in
aesmatch your data - Data types are appropriate for the geometry
- Required aesthetics are provided (e.g.,
xandyfor most geoms)
Development
Running tests
pytest
Code formatting
black src/
ruff check src/
License
MIT
Contributing
Contributions welcome! Please open an issue or submit a pull request.