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VisiData MCP Server

by moeloubani
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Python
Local

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.

🔗 Links

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