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

📊 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

🎨 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.

🔗 Links

• VisiData Website
• Model Context Protocol
• GitHub Repository

📦 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")

🔧 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')"