Fast PyAirbyte

Fast-PyAirbyte lets you generate a data pipeline as code, for any Airbyte Connector, using a single prompt:

"create a data pipeline from source-xxx to destination-xxx"

One click install

If the button above doesn't work, copy and paste this link into your browser:

cursor://anysphere.cursor-deeplink/mcp/install?name=fast-pyairbyte&config=eyJjb21tYW5kIjoibnB4IiwiYXJncyI6WyJmYXN0LXB5YWlyYnl0ZSJdLCJlbnYiOnsiT1BFTkFJX0FQSV9LRVkiOiJ5b3VyLW9wZW5haS1hcGkta2V5LWhlcmUifX0=

Or install manually using the instructions below.

Manual Installation

The easiest way to get started is using npx to run the MCP server directly:

npx fast-pyairbyte

This will:

1. Download and install the package automatically
2. Check for Python and install dependencies
3. Start the MCP server locally
4. Display configuration instructions

What is Fast PyAirbyte?

Fast-PyAirbyte lets you generate a data pipeline as code, for any Airbyte Connector, using a single prompt:

"create a data pipeline from source-xxx to destination-xxx"

It leverages OpenAI and connector documentation to help users quickly scaffold and configure data pipelines between sources and destinations supported by Airbyte. The MCP server automates code generation, provides context-aware guidance, and streamlines the process of building and deploying data pipelines.

• Generates PyAirbyte pipeline code based on user instructions and connector documentation
• Uses OpenAI and file search to provide context-aware code and instructions
• Available as an npm package that can be executed via npx
• Easy installation with no local setup required

MCP Configuration

Add this to your MCP configuration file:

For Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "fast-pyairbyte": {
      "command": "npx",
      "args": ["fast-pyairbyte"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

For Claude Desktop (~/.config/claude/claude_desktop_config.json):

{
  "mcpServers": {
    "fast-pyairbyte": {
      "command": "npx",
      "args": ["fast-pyairbyte"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

For Cline (~/.config/cline/mcp_settings.json):

{
  "mcpServers": {
    "fast-pyairbyte": {
      "command": "npx",
      "args": ["fast-pyairbyte"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here"
      }
    }
  }
}

Requirements:

• Your own OpenAI API key from OpenAI Platform
• Python 3.7+ installed on your system
• Node.js 14+ for npx execution

Configuration Steps:

1. Get your OpenAI API key from OpenAI Platform
2. Create or edit your MCP configuration file
3. Add the configuration above with your actual OpenAI API key
4. Restart your MCP client (Cursor/Claude/Cline)
5. Start generating PyAirbyte pipelines!

Usage

Once configured, you can use the MCP server in your AI assistant by asking it to generate PyAirbyte pipelines.

🚀 How to Use

1. Verify Connection

• Look for the MCP server status in your client's interface
• You should see "fast-pyairbyte" listed with 1 tool available
• If it shows 0 tools or is red, check your configuration

2. Generate Pipelines with Natural Language

Simply ask your AI assistant to generate a PyAirbyte pipeline! Here are example prompts:

Basic Examples:

Generate a PyAirbyte pipeline from source-postgres to destination-snowflake

Create a pipeline to move data from source-github to dataframe

Build a PyAirbyte script for source-stripe to destination-bigquery

Generate a data pipeline from source-salesforce to destination-postgres

Create a pipeline that reads from source-github to a dataframe, and then visualize the results using Streamlit

Help me set up a data pipeline from source-salesforce to destination-postgres

3. Available Source/Destination Options

• Sources: Any Airbyte source connector (e.g., source-postgres, source-github, source-stripe, source-mysql, source-salesforce)
• Destinations: Any Airbyte destination connector (e.g., destination-snowflake, destination-bigquery, destination-postgres) OR dataframe for Pandas analysis

4. Pro Tips

• Use "dataframe" as destination if you want to analyze data in Python/Pandas
• Be specific about your source and destination names (use official Airbyte connector names with source- or destination- prefixes)
• Ask follow-up questions if you need help with specific configuration or setup

The tool will automatically use your OpenAI API key (configured in the MCP settings) to generate enhanced, well-documented pipeline code with best practices and detailed setup instructions!

Features

• Automated Code Generation: Creates complete PyAirbyte pipeline scripts
• Configuration Management: Handles environment variables and credentials securely
• Documentation Integration: Uses OpenAI to provide context-aware instructions
• Multiple Output Formats: Supports both destination connectors and DataFrame output
• Best Practices: Includes error handling, logging, and proper project structure
• 600+ Connectors: If it's in the Airbyte Connector Registry, the MCP server can create pipelines for it
• Easy Installation: No local setup required - just use npx
• Cross-Platform: Works on macOS, Linux, and Windows

Available Tools

fast_pyairbyte

Creates a complete data pipeline using PyAirbyte and fast-pyairbyte to extract, transform, and load data between sources and destinations.

Parameters:

• source_name: The official Airbyte source connector name (e.g., 'source-postgres', 'source-github')
• destination_name: The official Airbyte destination connector name (e.g., 'destination-postgres', 'destination-snowflake') OR 'dataframe' to output to Pandas DataFrames

Returns:

• Complete Python pipeline code
• Setup and installation instructions
• Environment variable templates
• Best practices and usage guidelines

Development

Local Development

If you want to contribute or modify the server:

1. Clone the repository:

   git clone https://github.com/quintonwall/fast-pyairbyte.git
   cd fast-pyairbyte
   

2. Install dependencies:

   npm install
   

3. Test locally:

   npm start
   

Project Structure

fast-pyairbyte/
├── package.json              # npm package configuration
├── bin/
│   └── fast-pyairbyte.js     # Node.js launcher script
├── python/
│   ├── main.py               # Python MCP server
│   ├── telemetry.py          # Usage analytics
│   └── requirements.txt      # Python dependencies
├── README.md                 # This file
└── docs/                     # Documentation

Publishing

To publish a new version to npm:

npm version patch  # or minor/major
npm config set strict-ssl false
npm publish

Security & Privacy

• API Key Security: OpenAI API keys are passed securely through MCP environment variables
• No Data Storage: The server doesn't store any user data or credentials
• Anonymous Telemetry: Basic usage analytics are collected (can be disabled with DO_NOT_TRACK=1)
• Open Source: Full source code is available for inspection

Troubleshooting

Common Issues

1. "Python not found" error

   • Install Python 3.7+ from python.org
   • Ensure Python is in your system PATH

2. "Dependencies failed to install" error

   • Check your internet connection
   • Try running pip install --upgrade pip first

3. "OpenAI API key not found" error

   • Verify your API key is correctly set in the MCP configuration
   • Check that you're using a valid OpenAI API key

4. MCP server shows 0 tools

   • Check the MCP configuration file syntax
   • Restart your MCP client after configuration changes
   • Check the server logs for error messages

Getting Help

• Issues: Report bugs on GitHub Issues
• Discussions: Join the conversation on GitHub Discussions
• Slack: Ask questions in the Airbyte Slack

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests to help improve the PyAirbyte MCP Server.