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How to generate synthetic teacher documents as test fixtures with Yowes

Render labeled sample teacher ID cards and letters to test a document-processing pipeline.

Reverse engineering. The output is designed to look like real teacher credentials, so keep every file inside the test environment, label it as synthetic, and never present it as a genuine document.

Who it is forA QA or data engineer building or testing a document-intake or OCR pipeline who needs realistic, non-real sample images that contain no real person's data.

The problemTesting document-handling code needs many varied sample images, and real records cannot be used because of privacy and compliance limits.

The outcomeA folder of PNG sample documents across several countries and document types, produced locally and kept out of version control.

What you need first

  • Python 3.10 or newer
  • pip, or uv for the uvx route
  • An MCP client that speaks stdio, such as the clients named in the README
  • A test environment with synthetic data only

Steps

  1. Install from source with pip install -e .
  2. Check that bundled fonts and photos resolve with python -c "from countries.utils import load_font, get_profile_photo; print(load_font(30).getname()); print(get_profile_photo((280,340), person_id='x', gender='Male') is not None)" (inferred, check the README)
  3. Start the server over stdio with uvx --from . yowes-mcp
  4. From your MCP client, call list_countries_tool and list_schools to choose a country and school
  5. Generate the document types you need, then read the rendered PNGs from the output/ folder and label them as synthetic in your test records

Prompt for your agent

Use the Yowes MCP server in the hirotomasato/yowes repo to produce synthetic teacher documents for <country code> in a test-only folder. Call list_countries_tool and list_schools first, then generate one ID card and one employment letter as PNG files. Verify that each PNG opens and that the file names mark the documents as synthetic.

Caveat. The README does not describe watermarks or any marker that identifies outputs as fake, so labeling and storage control must be done by you.

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