CSV Editor

# CSV Editor - AI-Powered CSV Processing via MCP [](https://www.python.org/) [](https://modelcontextprotocol.io/) [](LICENSE) [](https://github.com/jlowin/fastmcp) [](https://pandas.pydata.org/) [](https://smithery.ai/server/@santoshray02/csv-editor) **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. <a href="https://glama.ai/mcp/servers/@santoshray02/csv-editor"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@santoshray02/csv-editor/badge" alt="CSV Editor MCP server" /> </a> ## 🎯 Why CSV Editor? ### The Problem AI assistants struggle with complex data operations - they can read files but lack tools for filtering, transforming, analyzing, and validating CSV data efficiently. ### The Solution 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 ### Key Differentiators | Feature | CSV Editor | Traditional Tools | |---------|-----------|------------------| | **AI Integration** | Native MCP protocol | Manual operations | | **Auto-Save** | Automatic with strategies | Manual save required | | **History Tracking** | Full undo/redo with snapshots | Limited or none | | **Session Management** | Multi-user isolated sessions | Single user | | **Data Validation** | Built-in quality scoring | Separate tools needed | | **Performance** | Handles GB+ files with chunking | Memory limitations | ## ⚡ Quick Demo ```python # 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! ``` ## 🚀 Quick Start (2 minutes) ### Installing via Smithery To install csv-editor for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@santoshray02/csv-editor): ```bash npx -y @smithery/cli install @santoshray02/csv-editor --client claude ``` ### Fastest Installation (Recommended) ```bash # 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 ``` ### Configure Your AI Assistant <details> <summary><b>Claude Desktop</b> (Click to expand)</summary> Add to `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS): ```json { "mcpServers": { "csv-editor": { "command": "uv", "args": ["tool", "run", "csv-editor"], "env": { "CSV_MAX_FILE_SIZE": "1073741824" } } } } ``` </details> <details> <summary><b>Other Clients</b> (Continue, Cline, Windsurf, Zed)</summary> See [MCP_CONFIG.md](MCP_CONFIG.md) for detailed configuration. </details> ## 💡 Real-World Use Cases ### 📊 Data Analyst Workflow ```python # 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") ``` ### 🏭 ETL Pipeline ```python # 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 ``` ### 🔍 Data Quality Assurance ```python # 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"]) ``` ## 🎨 Core Features ### Data Operations - **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 ### Analysis Tools - **Statistics**: Descriptive stats, correlations, distributions - **Outliers**: IQR, Z-score, custom thresholds - **Profiling**: Complete data quality reports - **Validation**: Schema checking, quality scoring ### Productivity Features - **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 ## 📚 Available Tools <details> <summary><b>Complete Tool List</b> (40+ tools)</summary> ### I/O Operations - `load_csv` - Load from file - `load_csv_from_url` - Load from URL - `load_csv_from_content` - Load from string - `export_csv` - Export to various formats - `get_session_info` - Session details - `list_sessions` - Active sessions - `close_session` - Cleanup ### Data Manipulation - `filter_rows` - Complex filtering - `sort_data` - Multi-column sort - `select_columns` - Column selection - `rename_columns` - Rename columns - `add_column` - Add computed columns - `remove_columns` - Remove columns - `update_column` - Update values - `change_column_type` - Type conversion - `fill_missing_values` - Handle nulls - `remove_duplicates` - Deduplicate ### Analysis - `get_statistics` - Statistical summary - `get_column_statistics` - Column stats - `get_correlation_matrix` - Correlations - `group_by_aggregate` - Group operations - `get_value_counts` - Frequency counts - `detect_outliers` - Find outliers - `profile_data` - Data profiling ### Validation - `validate_schema` - Schema validation - `check_data_quality` - Quality metrics - `find_anomalies` - Anomaly detection ### Auto-Save & History - `configure_auto_save` - Setup auto-save - `get_auto_save_status` - Check status - `undo` / `redo` - Navigate history - `get_history` - View operations - `restore_to_operation` - Time travel </details> ## ⚙️ Configuration ### Environment Variables | Variable | Default | Description | |----------|---------|-------------| | `CSV_MAX_FILE_SIZE` | 1GB | Maximum file size | | `CSV_SESSION_TIMEOUT` | 3600s | Session timeout | | `CSV_CHUNK_SIZE` | 10000 | Processing chunk size | | `CSV_AUTO_SAVE` | true | Enable auto-save | ### Auto-Save Strategies 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 ```python # Configure auto-save configure_auto_save( strategy="backup", backup_dir="/backups", max_backups=10 ) ``` ## 🛠️ Advanced Installation Options <details> <summary><b>Alternative Installation Methods</b></summary> ### Using pip ```bash git clone https://github.com/santoshray02/csv-editor.git cd csv-editor pip install -e . ``` ### Using pipx (Global) ```bash pipx install git+https://github.com/santoshray02/csv-editor.git ``` ### From GitHub (Recommended) ```bash # 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 ``` </details> ## 🧪 Development ### Running Tests ```bash uv run test # Run tests uv run test-cov # With coverage uv run all-checks # Format, lint, type-check, test ``` ### Project Structure ``` 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 ``` ## 🤝 Contributing We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. ### Quick Contribution Guide 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 ## 📈 Roadmap - [ ] SQL query interface - [ ] Real-time collaboration - [ ] Advanced visualizations - [ ] Machine learning integrations - [ ] Cloud storage support - [ ] Performance optimizations for 10GB+ files ## 💬 Support - **Issues**: [GitHub Issues](https://github.com/santoshray02/csv-editor/issues) - **Discussions**: [GitHub Discussions](https://github.com/santoshray02/csv-editor/discussions) - **Documentation**: [Wiki](https://github.com/santoshray02/csv-editor/wiki) ## 📄 License MIT License - see [LICENSE](LICENSE) file ## 🙏 Acknowledgments Built with: - [FastMCP](https://github.com/jlowin/fastmcp) - Fast Model Context Protocol - [Pandas](https://pandas.pydata.org/) - Data manipulation - [NumPy](https://numpy.org/) - Numerical computing --- **Ready to supercharge your AI's data capabilities?** [Get started in 2 minutes →](#-quick-start-2-minutes)