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gabcoyne

airflow-unfactor

by gabcoyne

generate_migration_report

Document Airflow-to-Prefect conversion decisions in MIGRATION.md and generate a before-production checklist with Prefect doc links.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_pathYes
decisionsYes
flow_pathYes
manual_actionsNo
output_directoryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full behavioral burden. It discloses the write side effect (MIGRATION.md), describes the produced checklist, mentions doc links, and notes a suggestion to add the Prefect MCP server. It does not explicitly address overwrites or file permissions, but the core behaviors are well covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: a one-line purpose, a usage positioning sentence, and organized Args/Returns sections. Every sentence adds meaningful information, and the length is justified by the need to document five parameters and return fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 params, no annotations, no schema descriptions), the description is remarkably complete. It covers purpose, when to call it, parameter semantics, and return value shape. The presence of an output schema means the return format does not need further elaboration.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. The Args section explains every parameter, including the structure of the 'decisions' list and the meaning of 'manual_actions'. This fully bridges the gap left by the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Write MIGRATION.md — human-readable record of a DAG conversion.' It distinguishes itself from siblings by stating it is the final step after generate_deployment and focuses on documentation/reporting rather than transformation or validation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs 'Call as the final step after generate_deployment,' providing clear when-to-use context. It does not list exclusions or direct alternatives, but the pipeline positioning sufficiently clarifies its role relative to siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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