Fast PyAirbyte

Provides automated PyAirbyte pipeline generation for Airbyte sources and destinations, plus DataFrame output

python

4

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

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

Fast-PyAirbyte MCP Server helps you generate Airbyte-based data pipelines by using a single prompt inside your MCP-enabled assistant. It automates code generation, provides context-aware guidance, and lets you quickly scaffold pipelines between any Airbyte source and destination, or output to a Pandas DataFrame for analysis.

How to use

Set up the MCP server in your AI assistant and verify it appears as an available tool named fast_pyairbyte. When you want to create a pipeline, tell your assistant exactly which source and destination to use, for example: Generate a PyAirbyte pipeline from source-postgres to destination-snowflake or Create a pipeline from source-github to dataframe. The MCP server will generate complete pipeline code, include setup guidance, and provide best practices and error handling. Use dataframe if you want to analyze results in Python/Pandas.

How to install

Prerequisites you need: Node.js 14+ and Python 3.7+. You also need your OpenAI API key to enable generation features.

Install using a local, zero-setup flow with npx to run the MCP server directly.

npx fast-pyairbyte

Configuration and usage notes

Configure the MCP client to expose the fast_pyairbyte server as an stdio MCP endpoint. The server requires your OpenAI API key to function. You will set the API key in your MCP configuration file.

Example MCP configuration (stdio) to run the server locally with npx, including the required environment variable.

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

Security, troubleshooting, and best practices

Keep your OpenAI API key secure and do not share it in logs or screenshots. If you encounter issues, verify that the MCP client recognizes the fast_pyairbyte tool and that the OpenAI API key is correctly set in your MCP configuration. If the tool shows 0 available, restart your MCP client after updating the config and check for typos in the server name or environment variables.

Best practices include providing precise source and destination names (using official Airbyte connector names with the source- or destination- prefixes), and using dataframe as a destination when you want to inspect results in Python.

Notes

This MCP server generates PyAirbyte pipeline code based on your instructions and Airbyte connector documentation, leveraging OpenAI and file search to tailor code and setup steps for your pipeline.

Available tools

fast_pyairbyte

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