Engineer Your Data Mcp

Engineer Your Data

[](https://registry.modelcontextprotocol.io/v0/servers?search=engineer-your-data) [](https://pypi.org/project/engineer-your-data/)

A Model Context Protocol (MCP) server designed specifically for data engineers and business intelligence professionals. Transform your data pipelines and BI workflows with AI-assisted data engineering capabilities that run locally without internet dependency.

Why Engineer Your Data?

Built from the ground up for data engineering teams and BI analysts who need:

• Pipeline Development - Build and test ETL/ELT transformations
• Data Quality Assurance - Profile and validate data sources
• Business Intelligence - Create analytics models and dashboard visualizations
• Local Control - Keep sensitive data on-premises with no cloud dependencies

🚀 Quick Start

New to Engineer Your Data? Start with these 5 essential operations:

1. Check Data Quality: "Generate a data quality report for my sales.csv file"
2. Find Issues: "Check for null values in the customer_data.csv"
3. Transform Data: "Filter the orders.csv for rows where status is 'completed'"
4. Visualize: "Create a bar chart showing sales by region from revenue.csv"
5. Summarize: "Give me a statistical summary of the dataset"

These cover 80% of daily data engineering tasks. Explore the full capabilities below!

Core Capabilities

🚀 File Operations:

• read_file - Read data files from local filesystem
• write_file - Write processed data to files
• list_files - Browse and discover data files
• file_info - Get metadata about data files

📊 Data Validation & Quality:

• validate_schema - Validate data against expected schemas
• check_nulls - Analyze null values and missing data patterns
• data_quality_report - Comprehensive data quality assessment
• detect_duplicates - Find duplicate records with configurable matching

🔄 Data Transformation:

• filter_data - Filter datasets based on conditions
• aggregate_data - Group and aggregate data with statistical functions
• join_data - Join multiple datasets with flexible join types
• pivot_data - Reshape data from long to wide format
• clean_data - Clean and standardize data values

📈 Visualization & Analysis:

• create_chart - Generate bar, pie, line, scatter, histogram, box, and heatmap charts
• data_summary - Create comprehensive dataset summaries with statistics
• export_visualization - Export charts and data to JSON, CSV, HTML, Markdown

🌐 API Integration:

• fetch_api_data - Retrieve data from REST APIs
• monitor_api - Monitor API endpoints for health and performance
• batch_api_calls - Execute multiple API calls efficiently
• api_auth - Manage API authentication

🔧 Utilities:

• chain_tools - Execute multiple tools in sequence
• analyze_schema - Analyze and understand data schemas

Quick Start for Data Teams

Installation

# Option 1: Install from PyPI (recommended)
pip install engineer-your-data

# Option 2: Install from source
git clone https://github.com/eghuzefa/engineer-your-data-mcp.git
cd engineer-your-data-mcp
pip install -e .

Configure for Your Data Environment

For PyPI Installation: Add to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "engineer-your-data": {
      "command": "python",
      "args": ["-m", "src.server"],
      "env": {
        "WORKSPACE_PATH": "/path/to/your/data/workspace"
      }
    }
  }
}

For Source Installation: Add to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "engineer-your-data": {
      "command": "python",
      "args": ["/path/to/engineer-your-data-mcp/src/server.py"],
      "env": {
        "WORKSPACE_PATH": "/path/to/your/data/workspace"
      }
    }
  }
}

Data Engineering Examples

Data Quality Analysis:

"Check the customer data for null values and duplicates"
"Generate a comprehensive data quality report for the sales dataset"
"Validate this CSV file against our customer schema"

Data Transformation:

"Filter the orders data for customers in the US region"
"Aggregate sales data by month and calculate total revenue"
"Join customer data with order data on customer_id"
"Pivot the sales data to show products as columns"

Visualization & Reporting:

"Create a bar chart showing revenue by department"
"Generate a summary of the dataset with key statistics"
"Export the sales analysis as an HTML report"

API Data Integration:

"Fetch customer data from the CRM API"
"Monitor the data pipeline API for health status"
"Authenticate with the analytics API using OAuth"

Architecture for Data Teams

Claude Desktop → MCP Protocol → Engineer Your Data → Local Python Environment
                                        ↓
                    pandas + numpy + requests + matplotlib
                                        ↓
                         Local Files + APIs + Data Sources

Testing & Quality

• 161 comprehensive tests with 100% pass rate
• Async/await support for high-performance operations
• Error handling with detailed logging and debugging
• Type safety with proper schema validation

# Run all tests
python -m pytest

# Run with coverage
python -m pytest --cov=src

# Run specific tool tests
python -m pytest tests/tools/test_visualization.py

Available Tools (17 Total)

File Operations (4 tools)

| Tool | Description | |------|-------------| | read_file | Read and parse data files (CSV, JSON, etc.) | | write_file | Write data to files with format options | | list_files | Directory browsing and file discovery | | file_info | File metadata and basic statistics |

Data Validation (4 tools)

| Tool | Description | |------|-------------| | validate_schema | Schema validation with custom rules | | check_nulls | Null value analysis and patterns | | data_quality_report | Comprehensive quality assessment | | detect_duplicates | Duplicate detection with flexible matching |

Data Transformation (5 tools)

| Tool | Description | |------|-------------| | filter_data | Advanced filtering with conditions | | aggregate_data | Grouping and statistical aggregation | | join_data | Multi-dataset joins (inner, outer, left, right) | | pivot_data | Data reshaping and pivoting | | clean_data | Data cleaning and standardization |

Visualization (3 tools)

| Tool | Description | |------|-------------| | create_chart | 7 chart types with customization | | data_summary | Statistical summaries and insights | | export_visualization | Multi-format export capabilities |

API Integration (4 tools)

| Tool | Description | |------|-------------| | fetch_api_data | REST API data retrieval | | monitor_api | API health monitoring | | batch_api_calls | Efficient bulk API operations | | api_auth | Authentication management |

Data Engineering Best Practices

• Sandboxed Execution - Safe environment for testing transformations
• Local Data Control - Keep sensitive data on your infrastructure
• Comprehensive Testing - All tools thoroughly tested and validated
• Enterprise Security - No external API calls for core functionality
• Performance Optimized - Async operations and efficient data processing

Integration with Your Stack

Works seamlessly alongside:

• dbt - Use for complex transformation logic development
• Airflow/Prefect - Incorporate into existing workflow orchestration
• Jupyter/Notebooks - Prototype and iterate on data transformations
• BI Tools - Generate data and visualizations for Tableau, Power BI, etc.
• APIs - Integrate with REST APIs and microservices

Contributing

Data engineers and BI professionals welcome! Please read our contributing guidelines and submit PRs for new data connectors, transformations, or BI features.

MCP Registry

This server is available in the official Model Context Protocol Registry.

License

MIT License - see LICENSE file for details.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: eghuzefa
• Source: eghuzefa/engineer-your-data-mcp
• License: MIT

Install and usage instructions live in the source repository linked above.