visidata-mcp 0.1.2

MCP server for VisiData - a terminal spreadsheet multitool for discovering and arranging tabular data

VisiData MCP Server

A Model Context Protocol (MCP) server that provides access to VisiData functionality. VisiData is a terminal spreadsheet multitool for discovering and arranging tabular data.

Features

This MCP server exposes VisiData's powerful data manipulation capabilities through the following tools:

🔧 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
• get_supported_formats - List all supported file formats

📚 Resources

• visidata://help - Comprehensive help documentation and usage examples

🎯 Prompts

• analyze_dataset_prompt - Generate structured prompts for comprehensive dataset analysis

Supported Data Formats

VisiData supports a wide variety of 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
• Python: Pickle files

Installation

🚀 Quick Install (Recommended)

The easiest way to install visidata-mcp is via npm. This automatically handles Python dependencies and setup:

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

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

That's it! The npm package automatically:

• ✅ Checks for Python 3.10+
• ✅ Installs the Python package and all dependencies
• ✅ Creates a global visidata-mcp command
• ✅ Works with both Claude Desktop and Cursor

Alternative: Python-only Install

Install from PyPI

pip install visidata-mcp

Install from Source

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

Usage

With Claude Desktop (npm install)

After installing via npm, simply add this to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

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

With Cursor AI (npm install)

Create .cursor/mcp.json in your project:

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

Restart your AI application and you're ready to go! 🎉

Legacy Configuration (Python-only install)

Click to expand legacy configuration instructions

With Cursor AI

1. Navigate to your project directory and ensure the virtual environment is activated:

   cd /path/to/your/visidata-mcp
   source venv/bin/activate
   

2. Create/Edit Cursor MCP configuration at .cursor/mcp.json in your project:

   {
     "mcpServers": {
       "visidata": {
         "command": "/path/to/your/visidata-mcp/venv/bin/python",
         "args": ["-m", "visidata_mcp.server"],
         "cwd": "/path/to/your/visidata-mcp"
       }
     }
   }
   

   ⚠️ Important: Use the full path to your virtual environment's Python executable. This ensures Cursor uses the correct Python interpreter with all dependencies installed.

3. Restart Cursor completely (Cmd+Q and reopen)

4. Start using VisiData tools in your AI chat! Look for "Available MCP Tools" in the chat interface.

With Claude Desktop

Add the server to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

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

Direct Execution

# Run the server directly
visidata-mcp

# Or with Python module
python -m visidata_mcp.server

Development Mode

# Using MCP Inspector for debugging
npx @modelcontextprotocol/inspector visidata-mcp

Example Usage

Loading and Analyzing Data

# Load a CSV file
load_data("/path/to/sales_data.csv")

# Get a sample of the first 5 rows
get_data_sample("/path/to/sales_data.csv", 5)

# Perform comprehensive analysis
analyze_data("/path/to/sales_data.csv")

# Get statistics for a specific column
get_column_stats("/path/to/sales_data.csv", "revenue")

Data Transformation

# Convert CSV to JSON
convert_data("/path/to/data.csv", "/path/to/output.json")

# Filter data
filter_data("/path/to/sales_data.csv", "revenue", "greater_than", "1000", "/path/to/high_revenue.csv")

# Sort data by column
sort_data("/path/to/sales_data.csv", "date", False, "/path/to/sorted_data.csv")

Getting Help

# Access the help resource
# This will provide comprehensive documentation and examples

Troubleshooting

Common Issues

"0 tools available" in Cursor

• Solution: Ensure you're using the full path to your virtual environment's Python in the MCP configuration
• Example: Use /path/to/visidata-mcp/venv/bin/python instead of just python
• Restart Cursor completely after changing the configuration

VisiData Warning Messages

You may see warnings like:

setting unknown option confirm_overwrite

These warnings are harmless and don't affect functionality. They occur because the MCP server sets VisiData options that may not be recognized in all versions.

Server Not Starting

• Verify your virtual environment has all dependencies: pip list | grep visidata
• Check that Python 3.8+ is being used: python --version
• Try running the server directly: python -m visidata_mcp.server

Permission Errors

• Ensure the virtual environment Python executable is accessible
• Check file permissions on your project directory
• Try running with python -m visidata_mcp.server to test manually

Testing Your Installation

Run the comprehensive verification script to check your entire setup:

cd /path/to/visidata-mcp
source venv/bin/activate
python verify_setup.py

This script will check:

• Python version and virtual environment
• Package installations
• MCP server tools registration (should show 8 tools)
• Configuration files
• Server startup capability

For just testing tools registration:

python test_tools.py

API Reference

Tools

load_data(file_path: str, file_type: Optional[str] = None) -> str

Load and inspect a data file.

Parameters:

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

Returns: JSON string with file information (rows, columns, column names, types)

get_data_sample(file_path: str, rows: int = 10) -> str

Get a sample of data from the file.

Parameters:

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

Returns: JSON string with sample data and metadata

analyze_data(file_path: str) -> str

Perform comprehensive data analysis.

Parameters:

• file_path: Path to the data file

Returns: JSON string with detailed analysis including column types and sample values

convert_data(input_path: str, output_path: str, output_format: Optional[str] = None) -> str

Convert data between formats.

Parameters:

• input_path: Path to input file
• output_path: Path for output file
• output_format: Target format (inferred from extension if not provided)

Returns: Success message or error details

filter_data(file_path: str, column: str, condition: str, value: str, output_path: Optional[str] = None) -> str

Filter data based on conditions.

Parameters:

• 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: JSON string with filtering results

get_column_stats(file_path: str, column: str) -> str

Get statistics for a specific column.

Parameters:

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

Returns: JSON string with column statistics

sort_data(file_path: str, column: str, descending: bool = False, output_path: Optional[str] = None) -> str

Sort data by a column.

Parameters:

• 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: JSON string with sorting results

get_supported_formats() -> str

Get list of supported file formats.

Returns: JSON string with supported formats and descriptions

Error Handling

The server includes comprehensive error handling:

• File Access Errors: Clear messages when files cannot be read
• Format Errors: Helpful messages for unsupported or corrupted files
• Processing Errors: Detailed error information with stack traces for debugging
• Validation Errors: Clear messages for invalid parameters or conditions

Performance Considerations

• Large Files: The server handles large datasets efficiently through VisiData's streaming capabilities
• Memory Usage: VisiData uses lazy loading and efficient data structures
• Batch Operations: Operations are optimized for batch processing

Development

Project Structure

visidata-mcp/
├── src/
│   └── visidata_mcp/
│       ├── __init__.py
│       └── server.py
├── pyproject.toml
├── README.md
└── requirements.txt

Building and Testing

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Build package
python -m build

# Run with debugging
python -m visidata_mcp.server

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

About VisiData

VisiData is an interactive multitool for tabular data. It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python, into a lightweight utility that can handle millions of rows with ease.

Key VisiData features exposed through this MCP server:

• Universal Data Loader: Open data from any format or source
• Efficient Processing: Handle large datasets with streaming and lazy evaluation
• Rich Type System: Automatic type detection and conversion
• Powerful Filtering: Complex filtering and selection capabilities
• Format Conversion: Convert between dozens of data formats

License

This project is licensed under the MIT License - see the LICENSE file for details.

Links

• VisiData Website
• VisiData Documentation
• Model Context Protocol
• MCP Specification

Support

For issues and questions:

• VisiData Issues: Related to data processing functionality
• MCP Issues: Related to the Model Context Protocol integration
• General Issues: Use the GitHub issue tracker

Changelog

Version 0.1.0

• Initial release
• Core VisiData functionality exposed through MCP
• Support for major data formats
• Comprehensive data analysis tools
• Format conversion capabilities
• Data filtering and sorting
• Column statistics and analysis