CSV Editor

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

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

# 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)

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

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

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

See MCP_CONFIG.md for detailed configuration.

💡 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

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

⚙️ 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

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

🛠️ Advanced Installation Options

Using pip

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

Using pipx (Global)

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

From GitHub (Recommended)

# 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

Fork the repository
Create a feature branch
Make your changes with tests
Run uv run all-checks
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 →

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Comprehensive CSV processing MCP server with 40+ operations for data manipulation, analysis, and validation. Features auto-save, undo/redo, and handles GB+ files

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