CSV Editor - AI-Powered CSV Processing via MCP

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.

🎯 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

⚡ Quick Demo

# 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:

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

Fastest Installation (Recommended)

# 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

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

💡 Real-World Use Cases

📊 Data Analyst Workflow

# 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

# 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

# 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

⚙️ Configuration

Environment Variables

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

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

🛠️ Advanced Installation Options

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

🧪 Development

Running Tests

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 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
• Discussions: GitHub Discussions
• Documentation: Wiki

📄 License

MIT License - see LICENSE file

🙏 Acknowledgments

Built with: - FastMCP - Fast Model Context Protocol - Pandas - Data manipulation - NumPy - Numerical computing

Ready to supercharge your AI's data capabilities? Get started in 2 minutes →