airflow-unfactor 1.0.0

MCP server for LLM-assisted conversion of Apache Airflow DAGs to Prefect flows

airflow-unfactor

An MCP server that converts Apache Airflow DAGs into Prefect flows. Point it at a DAG, and the LLM generates idiomatic Prefect code. Not a template with TODOs — working code. Built with FastMCP.

How It Works

The server exposes seven tools over MCP. The LLM reads raw DAG source code, looks up translation knowledge, and generates the Prefect flow.

Tool	What It Does
read_dag	Returns raw DAG source code with metadata (path, size, line count)
lookup_concept	Airflow→Prefect translation knowledge — operators, patterns, connections
validate	Syntax-checks generated code and returns both sources for comparison
search_prefect_docs	Searches live Prefect docs for anything not in the pre-compiled knowledge
scaffold	Creates a Prefect project directory structure (not code)
generate_deployment	Writes prefect.yaml deployment configuration from DAG metadata
generate_migration_report	Writes MIGRATION.md with conversion decisions and a before-production checklist

No AST parsing. No template engine. The LLM reads the code directly, just like a developer would.

Installation

# From PyPI
pip install airflow-unfactor

# Or with uv
uv pip install airflow-unfactor

Configuration

Claude Desktop

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

{
  "mcpServers": {
    "airflow-unfactor": {
      "command": "uvx",
      "args": ["airflow-unfactor"]
    }
  }
}

Claude Code

Add to .mcp.json in your project:

{
  "mcpServers": {
    "airflow-unfactor": {
      "command": "uvx",
      "args": ["airflow-unfactor"]
    }
  }
}

Cursor

Add to your Cursor MCP settings:

{
  "mcpServers": {
    "airflow-unfactor": {
      "command": "uvx",
      "args": ["airflow-unfactor"]
    }
  }
}

Then ask your LLM: "Convert the DAG in dags/my_etl.py to a Prefect flow."

Example

Airflow DAG:

from airflow import DAG
from airflow.operators.python import PythonOperator

def extract():
    return {"users": [1, 2, 3]}

def transform(ti):
    data = ti.xcom_pull(task_ids="extract")
    return [u * 2 for u in data["users"]]

with DAG("my_etl", ...) as dag:
    t1 = PythonOperator(task_id="extract", python_callable=extract)
    t2 = PythonOperator(task_id="transform", python_callable=transform)
    t1 >> t2

Generated Prefect flow:

from prefect import flow, task

@task
def extract():
    return {"users": [1, 2, 3]}

@task
def transform(data):
    return [u * 2 for u in data["users"]]

@flow(name="my_etl")
def my_etl():
    data = extract()
    result = transform(data)
    return result

The >> dependency chain becomes explicit data passing through return values. XCom is gone. It's just Python.

Translation Knowledge

The server ships with 78 pre-compiled Airflow→Prefect translation entries covering operators, patterns, connections, and core concepts. These are compiled by Colin from live Airflow source and Prefect documentation.

When the pre-compiled knowledge doesn't cover something, search_prefect_docs queries the Prefect documentation MCP server at docs.prefect.io in real time.

Documentation

Full docs: gabcoyne.github.io/airflow-unfactor

Development

git clone https://github.com/gabcoyne/airflow-unfactor.git
cd airflow-unfactor
uv sync

# Run tests
uv run pytest

# Lint
uv run ruff check --fix

# Compile translation knowledge
cd colin && colin run

License

MIT — see LICENSE.