Plot

claude mcp add --scope project clio-plot -- clio-kit mcp-server plot

Install the launcher first. Restart your client after setup. Configuration locations →

clio-kit doctor --server plot --connect

Tools​

line_plot

Create a line plot from CSV or Excel data with customizable styling.

bar_plot

Create a bar chart from CSV or Excel data, one bar per x value. Rows that share an x value are averaged (mean of y; the result reports aggregated=true and the y axis is labelled 'mean <y>'), keeping the 20 largest means; a file with one row per x value is plotted as-is (aggregated=false). For any other statistic, aggregate first and plot that file.

scatter_plot

Create a scatter plot from CSV or Excel data for correlation analysis.

histogram_plot

Create a histogram from CSV or Excel data showing value distribution.

heatmap_plot

Create a correlation heatmap from numeric columns in CSV or Excel data.

plot_timeseries

Create a multi-series line chart PNG from one or more y columns of a CSV or Excel file, auto-detecting a time, numeric, or categorical x axis.

data_info

Get schema, column types, and summary statistics for a CSV or Excel file.

Examples​

Replace example paths with your own files and check the returned results.

1. Data Exploration and Analysis​

I have a CSV file at /data/sales_data.csv with sales information. Can you first analyze the data structure and then create appropriate visualizations to show sales trends over time?

Tools called:

• data_info - Analyze the dataset structure
• line_plot - Create time-series plots showing sales trends

This prompt will:

• Use data_info to analyze the dataset structure
• Create time-series plots using line_plot showing sales trends
• Provide statistical insights about the data

2. Comparative Analysis with Multiple Charts​

Using the file /data/survey_results.csv, create a comprehensive analysis showing:
1. Age distribution of respondents (histogram)
2. Correlation between satisfaction scores (heatmap)  
3. Department vs average salary comparison (bar chart)

Tools called:

• histogram_plot - Age distribution of respondents
• heatmap_plot - Correlation between satisfaction scores
• bar_plot - Department vs average salary comparison

This prompt will:

• Generate multiple complementary visualizations
• Provide statistical analysis for each chart type
• Show data relationships and distributions
• Create professional publication-ready plots

3. Scientific Data Visualization​

I have temperature measurement data in /data/temperature.csv. Create a scatter plot showing the relationship between ambient temperature and device performance, and add a trend analysis.

Tools called:

• scatter_plot - Relationship between ambient temperature and device performance

This prompt will:

• Create correlation analysis between variables using scatter_plot
• Generate scatter plot with trend lines
• Provide statistical correlation metrics
• Include uncertainty analysis if applicable

4. Business Intelligence Dashboard​

From /data/quarterly_metrics.xlsx, create visualizations showing:
- Revenue trends by quarter (line plot)
- Performance metrics distribution (histogram)
- Regional comparison (bar chart)

Tools called:

• line_plot - Revenue trends by quarter
• histogram_plot - Performance metrics distribution
• bar_plot - Regional comparison

This prompt will:

• Handle Excel file format automatically
• Create multiple business-focused visualizations
• Provide executive summary statistics
• Generate dashboard-style layouts

5. Research Data Publication​

Using /data/experiment_results.csv, create publication-quality figures showing experimental conditions vs outcomes with proper error handling and statistical annotations.

Tools called:

• data_info - Analyze experimental data structure and quality
• scatter_plot - Show relationship between experimental conditions and outcomes
• heatmap_plot - Display correlation matrix of experimental variables

This prompt will:

• Use data_info to analyze data structure and handle missing values
• Generate scatter_plot for condition-outcome relationships
• Create heatmap_plot for correlation analysis
• Generate publication-ready 300 DPI plots
• Include proper statistical annotations

6. Quick Data Quality Check​

I need to quickly assess the quality of my dataset at /data/customer_data.csv - show me data completeness, distributions, and suggest the best visualization approaches.

Tools called:

• data_info - Comprehensive data quality assessment

This prompt will:

• Use data_info to perform comprehensive data quality assessment
• Identify missing values and data issues
• Suggest optimal visualization strategies
• Provide data cleaning recommendations