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Readme

VisiData MCP Server

A Model Context Protocol (MCP) server that provides access to VisiData functionality with enhanced data visualization and analysis capabilities.

🚀 Features

📊 Data Visualization

create_correlation_heatmap - Generate correlation matrices with beautiful heatmap visualizations
create_distribution_plots - Create statistical distribution plots (histogram, box, violin, kde)
create_graph - Custom graphs (scatter, line, bar, histogram) with categorical grouping support

🧠 Advanced Skills Analysis

parse_skills_column - Parse comma-separated skills into individual skills with one-hot encoding
analyze_skills_by_location - Comprehensive skills frequency and distribution analysis by location
create_skills_location_heatmap - Visual heatmap showing skills distribution across locations
analyze_salary_by_location_and_skills - Advanced salary statistics by location and skills combination

🔧 Core Data Tools

load_data - Load and inspect data files from various formats
get_data_sample - Get a preview of your data with configurable row count
analyze_data - Perform comprehensive data analysis with column types and statistics
convert_data - Convert between different data formats (CSV ↔ JSON ↔ Excel, etc.)
filter_data - Filter data based on conditions (equals, contains, greater/less than)
get_column_stats - Get detailed statistics for specific columns
sort_data - Sort data by any column in ascending or descending order

📦 Installation

🚀 Quick Install (Recommended)

npm install -g @moeloubani/visidata-mcp@beta

Prerequisites: Python 3.10+ (the installer will check and guide you if needed)

Alternative: Python Install

pip install visidata-mcp

Development Install

git clone https://github.com/moeloubani/visidata-mcp.git
cd visidata-mcp
pip install -e .

⚙️ Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "visidata": {
      "command": "visidata-mcp"
    }
  }
}

Cursor AI

Create .cursor/mcp.json in your project:

{
  "mcpServers": {
    "visidata": {
      "command": "visidata-mcp"
    }
  }
}

Restart your AI application after configuration changes.

🎯 Example Usage

Data Visualization

# Create a correlation heatmap
create_correlation_heatmap("sales_data.csv", "correlation_heatmap.png")

# Generate distribution plots for all numeric columns
create_distribution_plots("sales_data.csv", "distributions.png", plot_type="histogram")

# Create a scatter plot with categorical grouping
create_graph("sales_data.csv", "price", "sales", "scatter_plot.png", 
            graph_type="scatter", category_column="region")

Skills Analysis

# Parse comma-separated skills into individual columns
parse_skills_column("jobs.csv", "required_skills", "skills_parsed.csv")

# Analyze skills distribution by location
analyze_skills_by_location("jobs.csv", "required_skills", "location", "skills_analysis.json")

# Create skills-location heatmap
create_skills_location_heatmap("jobs.csv", "required_skills", "location", "skills_heatmap.png")

# Comprehensive salary analysis
analyze_salary_by_location_and_skills("jobs.csv", "salary", "location", "required_skills", "salary_analysis.xlsx")

Basic Data Operations

# Load and analyze data
load_data("data.csv")
get_data_sample("data.csv", 10)
analyze_data("data.csv")

# Transform data
convert_data("data.csv", "data.json")
filter_data("data.csv", "revenue", "greater_than", "1000", "high_revenue.csv")
sort_data("data.csv", "date", False, "sorted_data.csv")

📊 Supported Data Formats

Spreadsheets: CSV, TSV, Excel (XLSX/XLS)
Structured Data: JSON, JSONL, XML, YAML
Databases: SQLite
Scientific: HDF5, Parquet, Arrow
Archives: ZIP, TAR, GZ, BZ2, XZ
Web: HTML tables

🔧 Troubleshooting

Common Issues

"No module named 'matplotlib'"

Make sure you're using the correct MCP server path
For local development: /path/to/visidata-mcp/venv/bin/visidata-mcp
Restart your AI application after configuration changes

"0 tools available"

Verify the MCP server path in your configuration
Check that Python 3.10+ is installed
Restart your AI application completely

Verification

Test your installation:

# Check if server starts
visidata-mcp

# Test with Python
python -c "from visidata_mcp.server import main; print('✅ Server ready')"

🎨 Key Features

✅ Complete visualization support with matplotlib, seaborn, and scipy
✅ Advanced skills analysis for job market and HR data
✅ Skills-location correlation analysis and visualization
✅ Salary analysis by location and skills combination
✅ Enhanced error handling with dependency validation
✅ Publication-ready visualizations (300 DPI PNG output)

📈 Use Cases

Job Market Analysis

Skills demand analysis by geographic location
Salary benchmarking across locations and skill sets
Market trend visualization with correlation analysis

Data Science Workflows

Complete statistical analysis pipeline
Publication-ready visualizations
Advanced text processing for categorical data

Business Intelligence

Location-based performance analysis
Skills gap identification
Compensation analysis and benchmarking

🛠 Development

# Install for development
git clone https://github.com/moeloubani/visidata-mcp.git
cd visidata-mcp
pip install -e .

# Build package
python -m build

# Run tests
python -c "from visidata_mcp.server import main; print('✅ Ready')"

📄 License

MIT License - see LICENSE for details.

@moeloubani/visidata-mcp