Read an Airflow DAG file and return raw source with metadata.

Accepts a file path or inline content. Returns the source code, file path, size, and line count. The LLM reads the code directly.

Args: path: Path to a DAG file on disk. content: Inline DAG source code.

Returns: JSON with source, file_path, file_size_bytes, line_count — or error.

Look up Airflow→Prefect translation knowledge for a concept.

Searches Colin-compiled knowledge for operators, patterns, connections, and core concepts. Falls back to built-in mappings if Colin output is not available.

Args: concept: The Airflow concept to look up (e.g. "PythonOperator", "XCom", "TaskGroup", "postgres_default").

Returns: JSON with concept_type, airflow info, prefect_equivalent, translation_rules, and source ("colin" or "fallback").

Search current Prefect documentation via the Prefect MCP server.

For real-time queries beyond what Colin pre-compiled. Returns search results or an error with suggestion to run 'colin run'.

Args: query: Search query for Prefect docs.

Returns: JSON with search results or error.

Validate a converted Prefect flow against the original Airflow DAG.

Returns both source files for comparison plus a syntax check on the generated code. You perform the structural comparison.

Args: original_dag: Path or inline content of the original DAG. converted_flow: Path or inline content of the generated flow.

Returns: JSON with original_source, converted_source, syntax_valid, syntax_errors, and comparison_guidance.

Generate a Prefect project directory structure.

Creates the project skeleton following prefecthq/flows conventions. Does NOT generate flow code - that's for the LLM to do.

Args: output_directory: Where to create the project project_name: Project name (defaults to directory name) workspace: Workspace name for deployments// structure flow_names: List of flow names to create directories for include_docker: Include Dockerfile template include_github_actions: Include CI workflow template schedule_interval: Cron string, preset (@daily etc.), seconds, or None.

Returns: JSON with created_directories, created_files, prefect_yaml_template, next_steps

Write prefect.yaml deployment configuration from DAG metadata.

Call after generating flow.py. Produces a complete prefect.yaml with YAML anchors, schedule config, parameter defaults, and TODO stubs for work pool and pull step configuration.

Args: output_directory: Directory to write prefect.yaml into. flows: List of flow dicts. Each requires flow_name and entrypoint. Optional fields: schedule (cron/interval/None), parameters (dict of name→default), description, tags, dataset_triggers. workspace: Workspace name (default: "default").

Returns: JSON with created_file, deployment_names, next_steps.

Write MIGRATION.md — human-readable record of a DAG conversion.

Call as the final step after generate_deployment. Documents every conversion decision, produces a before-production checklist with Prefect doc links, and suggests adding the Prefect MCP server.

Args: output_directory: Directory to write MIGRATION.md into. dag_path: Path to the original Airflow DAG file. flow_path: Path to the generated Prefect flow file. decisions: List of dicts, each with: component, outcome, rationale (optional), manual_action (optional). manual_actions: Top-level action types not tied to a specific component (e.g. "setup_work_pool", "migrate_connections").

Returns: JSON with created_file, checklist_items_count.