Skip to main content
Glama

CSV Editor

by santoshray02

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

FeatureCSV EditorTraditional Tools
AI IntegrationNative MCP protocolManual operations
Auto-SaveAutomatic with strategiesManual save required
History TrackingFull undo/redo with snapshotsLimited or none
Session ManagementMulti-user isolated sessionsSingle user
Data ValidationBuilt-in quality scoringSeparate tools needed
PerformanceHandles GB+ files with chunkingMemory 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)

# 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

VariableDefaultDescription
CSV_MAX_FILE_SIZE1GBMaximum file size
CSV_SESSION_TIMEOUT3600sSession timeout
CSV_CHUNK_SIZE10000Processing chunk size
CSV_AUTO_SAVEtrueEnable 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
# 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

📄 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 →

Related MCP Servers

  • A
    security
    A
    license
    A
    quality
    An MCP server that provides tools for reading, writing, and editing files on the local filesystem.
    Last updated -
    1
    1,528
    Python
    Apache 2.0
    • Apple
  • A
    security
    F
    license
    A
    quality
    An MCP server that provides comprehensive Excel file operations, data analysis, and visualization capabilities for working with various spreadsheet formats like XLSX, CSV, and JSON.
    Last updated -
    8
    65
    Python
  • -
    security
    A
    license
    -
    quality
    Open source MCP server specializing in easy, fast, and secure tools for Databases.
    Last updated -
    9,196
    Go
    Apache 2.0
    • Linux
  • -
    security
    A
    license
    -
    quality
    An MCP server that manages chunking and reading of large responses, allowing tools to handle oversized data that would otherwise fail.
    Last updated -
    2
    Python
    GPL 3.0

View all related MCP servers

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/santoshray02/csv-editor'

If you have feedback or need assistance with the MCP directory API, please join our Discord server