Development workflow

Active work happens on the dev branch. Changes are merged into main when they are ready for release.

Directory layout

• mcp_project/ – source code for the package.
• requirements.txt – project dependencies.
• pyproject.toml – build configuration.
• tests/ – unit tests.
• .codex/ – persistent notes for @codex with extra context.

Show the project tree

Display the folder structure using:

python -m mcp_project.tree
# or
python -m mcp_project tree

Use ProjectTree in your code

You can also build the listing programmatically:

from mcp_project import ProjectTree

tree = ProjectTree(".")
for line in tree.build_tree():
    print(line)

Run tests

Execute the test suite with:

python -m pytest

======= 2. Construir la imagen Docker:

docker build -t imgprocessingcontainer .

3. Ejecutar el contenedor:

docker run -it --rm -v $(pwd):/workspace imgprocessingcontainer

4. Ejecutar el script principal:

python main.py -c config.yml

Puedes personalizar config.yml para adaptar el comportamiento del pipeline.

---

Ejemplo de config.yml

src: "./00_Imgs"
dst: "./output"

images:
  resize: [1024, 768]
  format: "png"
  quality: 80

icons:
  size: [64, 64]
  color: "#FF0000"

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Documentación

Consulta el directorio docs/guides/ para guias de uso y estilo. Los materiales del curso MCP estan en docs/course/.

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Licencia

Este proyecto está bajo la licencia MIT. Consulta el archivo LICENSE para más detalles.

Course Materials

This repository now focuses on the "MCP: Build Rich-Context AI Apps with Anthropic" course. See docs/course/mcp_course_overview.md for details and future steps.