VisiData MCP Server

- 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

- 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

- 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

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

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

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

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

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

Create.cursor/mcp.jsonin your project:

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

Restart your AI applicationafter configuration changes.

# 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")

# 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")

# 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")

- 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

"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

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

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

- ✅Complete visualization supportwith matplotlib, seaborn, and scipy
- ✅Advanced skills analysisfor job market and HR data
- ✅Skills-location correlationanalysis and visualization
- ✅Salary analysisby location and skills combination
- ✅Enhanced error handlingwith dependency validation
- ✅Publication-ready visualizations(300 DPI PNG output)

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

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

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

# 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')"

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A powerful server for Excel file processing, data analysis, and visualization, leveraging Python and Go for high performance.

Manipulate Excel files (XLSX, XLSM, etc.) without needing Microsoft Excel, featuring an integrated AI assistant.

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Interact with Google Sheets using a Python-based MCP server and Google Apps Script.

Full Google Sheets integration - read, write, format cells, create charts, use formulas, and manage spreadsheets.

Parses invoice data, uploads it to Google Sheets, and answers queries by fetching information from the sheet.

A data firewall between AI and your Google Sheets - Integrate with Google Sheets to read, write, and manage spreadsheet data.

A specialized Google Sheets integration server that allows the LLM to read, write, and manage spreadsheet data in real-time. This server supports cell-level manipulation, bulk range updates, and full worksheet retrieval, enabling the model to perform data analysis, logging, and automated reporting directly within Google Worksheets.If you have functions which take range value then first read the sheet and decide where user is asking to add data and define range by your own.Provides 46 tools for Gsheet