Transform how AI assistants work with CSV data. CSV Editor is a high-performance MCP server that gives Claude, ChatGPT, and other AI assistants powerful data manipulation capabilities through simple commands.

AI assistants struggle with complex data operations - they can read files but lack tools for filtering, transforming, analyzing, and validating CSV data efficiently.

CSV Editor bridges this gap by providing AI assistants with 40+ specialized tools for CSV operations, turning them into powerful data analysts that can:

• Clean messy datasets in seconds
• Perform complex statistical analysis
• Validate data quality automatically
• Transform data with natural language commands
• Track all changes with undo/redo capabilities

# Your AI assistant can now do this: "Load the sales data and remove duplicates" "Filter for Q4 2024 transactions over $10,000" "Calculate correlation between price and quantity" "Fill missing values with the median" "Export as Excel with the analysis" # All with automatic history tracking and undo capability!

To install csv-editor for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @santoshray02/csv-editor --client claude

# Install uv if needed (one-time setup) curl -LsSf https://astral.sh/uv/install.sh | sh # Clone and run git clone https://github.com/santoshray02/csv-editor.git cd csv-editor uv sync uv run csv-editor

{ "mcpServers": { "csv-editor": { "command": "uv", "args": ["tool", "run", "csv-editor"], "env": { "CSV_MAX_FILE_SIZE": "1073741824" } } } }

# Morning: Load yesterday's data session = load_csv("daily_sales.csv") # Clean: Remove duplicates and fix types remove_duplicates(session_id) change_column_type("date", "datetime") fill_missing_values(strategy="median", columns=["revenue"]) # Analyze: Get insights get_statistics(columns=["revenue", "quantity"]) detect_outliers(method="iqr", threshold=1.5) get_correlation_matrix(min_correlation=0.5) # Report: Export cleaned data export_csv(format="excel", file_path="clean_sales.xlsx")

# Extract from multiple sources load_csv_from_url("https://api.example.com/data.csv") # Transform with complex operations filter_rows(conditions=[ {"column": "status", "operator": "==", "value": "active"}, {"column": "amount", "operator": ">", "value": 1000} ]) add_column(name="quarter", formula="Q{(month-1)//3 + 1}") group_by_aggregate(group_by=["quarter"], aggregations={ "amount": ["sum", "mean"], "customer_id": "count" }) # Load to different formats export_csv(format="parquet") # For data warehouse export_csv(format="json") # For API

# Validate incoming data validate_schema(schema={ "customer_id": {"type": "integer", "required": True}, "email": {"type": "string", "pattern": r"^[^@]+@[^@]+\.[^@]+$"}, "age": {"type": "integer", "min": 0, "max": 120} }) # Quality scoring quality_report = check_data_quality() # Returns: overall_score, missing_data%, duplicates, outliers # Anomaly detection anomalies = find_anomalies(methods=["statistical", "pattern"])

• Load & Export: CSV, JSON, Excel, Parquet, HTML, Markdown
• Transform: Filter, sort, group, pivot, join
• Clean: Remove duplicates, handle missing values, fix types
• Calculate: Add computed columns, aggregations

• Statistics: Descriptive stats, correlations, distributions
• Outliers: IQR, Z-score, custom thresholds
• Profiling: Complete data quality reports
• Validation: Schema checking, quality scoring

• Auto-Save: Never lose work with configurable strategies
• History: Full undo/redo with operation tracking
• Sessions: Multi-user support with isolation
• Performance: Stream processing for large files

CSV Editor automatically saves your work with configurable strategies:

• Overwrite (default) - Update original file
• Backup - Create timestamped backups
• Versioned - Maintain version history
• Custom - Save to specified location

# Configure auto-save configure_auto_save( strategy="backup", backup_dir="/backups", max_backups=10 )

git clone https://github.com/santoshray02/csv-editor.git cd csv-editor pip install -e .

pipx install git+https://github.com/santoshray02/csv-editor.git

# Install latest version pip install git+https://github.com/santoshray02/csv-editor.git # Or using uv uv pip install git+https://github.com/santoshray02/csv-editor.git # Install specific version pip install git+https://github.com/santoshray02/csv-editor.git@v1.0.1

uv run test # Run tests uv run test-cov # With coverage uv run all-checks # Format, lint, type-check, test

csv-editor/ ├── src/csv_editor/ # Core implementation │ ├── tools/ # MCP tool implementations │ ├── models/ # Data models │ └── server.py # MCP server ├── tests/ # Test suite ├── examples/ # Usage examples └── docs/ # Documentation 

We welcome contributions! See CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Run uv run all-checks
5. Submit a pull request

• SQL query interface
• Real-time collaboration
• Advanced visualizations
• Machine learning integrations
• Cloud storage support
• Performance optimizations for 10GB+ files

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki

MIT License - see LICENSE file

Built with:

• FastMCP - Fast Model Context Protocol
• Pandas - Data manipulation
• NumPy - Numerical computing

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