Data Factory Flow

Use Managed Research data-factory tools for intake, generation, review, and publish flows.

Use the data-factory flow when the run should create or refine datasets, examples, labels, or research inputs before downstream work.

Goal

Start with clear intake criteria, generation constraints, review policy, and publish expectations.

MCP path

Ask your MCP client:

Trigger a Managed Research data-factory flow for this project. Use directed effort, provider openrouter, and require an artifact manifest plus final report before publish.

The primary MCP tool is smr_trigger_data_factory.

Good instructions

• describe the target dataset or artifact
• specify accepted source material
• specify validation or review criteria
• require rejected-example notes
• require artifact manifest and publish summary

Expected evidence

• generated or revised data files
• review notes
• rejected or deferred examples
• final report
• usage summary

Failure notes

Use project-scoped context for data-factory work. One-off runs are better for isolated repo tasks.