mvt-analyze-code

You can extract project context by scanning source directories to identify module boundaries, entry points, and domain terms. This process converts scattered implementation details into a centralized project context document containing entities, business rules, and API surfaces.

The best way to generate legacy documentation is running an automated codebase analysis that derives domain terms, entities, and API overviews directly from source code and comments. It produces a centralized markdown file mapping modules and business rules for reliable reference.

Yes, code analysis accelerates onboarding for multi-project repositories by scanning project directories to identify source roots and module boundaries. It generates a shared semantic map of domain terms and public interfaces, reducing ramp-up time for unfamiliar codebases.

You map API surfaces and module boundaries by applying structured scanning to analyze source files and comments. This extracts public interfaces and source paths, updating a project context file to prepare reliable inputs for downstream design and implementation work.

Code analysis applies guarded handling when source files or project registration are missing, ensuring the process completes without crashing. However, the generated project context document will lack full module mapping and API overview details for those unavailable paths.

You need a project context document to give AI collaborators a shared semantic map of modules, domain terms, and rules. It populates source paths in configuration files, enabling future AI workflows to reliably reference structured project context during generation tasks.