VisiData MCP Server

Load data from a file using VisiData.

Args: file_path: Path to the data file file_type: Optional file type hint (csv, json, xlsx, etc.)

Returns: String representation of the loaded data structure

Get a sample of data from a file.

Args: file_path: Path to the data file rows: Number of rows to return (default: 10)

Returns: Sample data in JSON format

Perform basic analysis on a dataset.

Args: file_path: Path to the data file

Returns: Analysis results including statistics and data types

Convert data from one format to another using pandas.

Args: input_path: Path to the input data file output_path: Path for the output file output_format: Target format (csv, json, xlsx, etc.)

Returns: Success message or error details

Filter data based on a condition.

Args: file_path: Path to the data file column: Column name to filter on condition: Filter condition (equals, contains, greater_than, less_than) value: Value to filter by output_path: Optional path to save filtered data

Returns: Information about the filtered data

Get statistics for a specific column.

Args: file_path: Path to the data file column: Column name to analyze

Returns: Column statistics in JSON format

Sort data by a specific column.

Args: file_path: Path to the data file column: Column name to sort by descending: Sort in descending order (default: False) output_path: Optional path to save sorted data

Returns: Information about the sorted data

Create a graph/plot from data using matplotlib/seaborn.

Args: file_path: Path to the data file x_column: Column name for x-axis (must be numeric) y_column: Column name for y-axis (must be numeric) output_path: Path where to save the graph image graph_type: Type of graph (scatter, line, bar, histogram) category_column: Optional categorical column for grouping/coloring

Returns: Information about the created graph

Create a correlation heatmap from numeric columns in the dataset.

Args: file_path: Path to the data file output_path: Path where to save the heatmap image columns: Optional list of specific columns to include (if None, uses all numeric columns)

Returns: Information about the created heatmap

Create distribution plots for numeric columns.

Args: file_path: Path to the data file output_path: Path where to save the distribution plots columns: Optional list of specific columns to plot (if None, uses all numeric columns) plot_type: Type of distribution plot (histogram, box, violin, kde)

Returns: Information about the created distribution plots

Get a list of supported file formats in VisiData.

Returns: List of supported formats and their descriptions

Parse comma-separated skills into individual skills and create one-hot encoding.

Args: file_path: Path to the data file skills_column: Column name containing comma-separated skills output_path: Optional path to save the processed data

Returns: Information about the parsed skills data

Analyze skills frequency and distribution by location.

Args: file_path: Path to the data file skills_column: Column name containing comma-separated skills location_column: Column name containing location information output_path: Optional path to save the analysis results

Returns: Skills analysis by location

Create a heatmap showing skills distribution across locations.

Args: file_path: Path to the data file skills_column: Column name containing comma-separated skills location_column: Column name containing location information output_path: Path where to save the heatmap image top_skills: Number of top skills to include (default: 15) top_locations: Number of top locations to include (default: 10)

Returns: Information about the created skills-location heatmap

Analyze salary statistics by location and skills combination.

Args: file_path: Path to the data file salary_column: Column name containing salary information location_column: Column name containing location information skills_column: Column name containing comma-separated skills output_path: Optional path to save the analysis results

Returns: Salary analysis by location and skills