Plotnine MCP Server

# 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 - **🎨 Multi-Layer Plots**: Combine multiple geometries in a single plot (scatter + trend lines, boxplots + jitter, etc.) - **Multiple Data Sources**: Load data from files (CSV, JSON, Parquet, Excel), URLs, or inline JSON - **Grammar of Graphics**: Compose plots using aesthetics, geometries, scales, themes, facets, and coordinates - **20+ Geometry Types**: Points, lines, bars, histograms, boxplots, violins, and more - **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 - **Multiple Output Formats**: PNG, PDF, SVG with configurable dimensions and DPI ## Installation ### 1. Clone or download this repository ```bash cd plotnine-mcp ``` ### 2. Install dependencies Using pip: ```bash pip install -e . ``` For full functionality (parquet and Excel support): ```bash pip install -e ".[full]" ``` ### 3. Configure Your MCP Client #### 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` ```json { "mcpServers": { "plotnine": { "command": "python", "args": ["-m", "plotnine_mcp.server"] } } } ``` If you installed in a virtual environment, use the full path to python: ```json { "mcpServers": { "plotnine": { "command": "/path/to/venv/bin/python", "args": ["-m", "plotnine_mcp.server"] } } } ``` #### Cursor Add to your Cursor settings by opening the command palette (`Cmd/Ctrl+Shift+P`) and searching for "Preferences: Open User Settings (JSON)". Add: ```json { "mcp.servers": { "plotnine": { "command": "python", "args": ["-m", "plotnine_mcp.server"] } } } ``` Or configure via `.cursor/mcp.json` in your project: ```json { "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` ```json { "mcpServers": { "plotnine": { "command": "python", "args": ["-m", "plotnine_mcp.server"] } } } ``` 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 ### create_plot Create a plotnine visualization with full customization. **Required Parameters:** - `data_source`: Data source configuration - `type`: "file", "url", or "inline" - `path`: File path or URL (for file/url types) - `data`: Array of objects (for inline type) - `format`: "csv", "json", "parquet", or "excel" (auto-detected) - `aes`: Aesthetic mappings (column names) - `x`, `y`: Axis variables - `color`, `fill`: Color aesthetics - `size`, `alpha`, `shape`, `linetype`: Additional aesthetics - `group`: Grouping variable - `geom`: Geometry specification - `type`: Geometry type (point, line, bar, etc.) - `params`: Additional parameters (size, alpha, color, etc.) **Optional Parameters:** - `scales`: Array of scale configurations - `theme`: Theme configuration with base and customizations - `facets`: Faceting configuration - `labels`: Plot labels (title, x, y, caption, subtitle) - `coords`: Coordinate system configuration - `stats`: Statistical transformations - `output`: Output configuration (format, size, DPI, directory) ### list_geom_types List all available geometry types with descriptions. ## 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 ```json { "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 ```json { "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 ```json { "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: ```json { "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: ```json { "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"** ## Configuration Options ### Themes Available base themes: - `gray` (default) - `bw` (black and white) - `minimal` - `classic` - `dark` - `light` - `void` ### 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: ```bash pip install -e . ``` ### Parquet/Excel support Install optional dependencies: ```bash 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 `aes` match your data - Data types are appropriate for the geometry - Required aesthetics are provided (e.g., `x` and `y` for most geoms) ## Development ### Running tests ```bash pytest ``` ### Code formatting ```bash black src/ ruff check src/ ``` ## License MIT ## Contributing Contributions welcome! Please open an issue or submit a pull request. ## Resources - [plotnine documentation](https://plotnine.readthedocs.io/) - [MCP specification](https://modelcontextprotocol.io/) - [Grammar of Graphics](https://ggplot2.tidyverse.org/)