Learn About MTT
"A picture paints a thousand words."
— Fred R. Barnard
Learn how to use Model Transform Tools (MTT) to generate API specifications, message schemas, and documentation from pictorial representations of your business information.
Getting Started
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MTT Overview
Understand the core architecture, transformation engine concepts, and execution pipeline.
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Project Structure
Explore repository layout, configuration files, and source control organization.
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Running the Tool
Execute transformations locally, via command line, or inside automated GitHub Actions.
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Example Projects
Browse live sample models and their generated OpenAPI specs, GraphQL schemas, and docs.
Technical References
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UML & Models
Guidelines for modeling domain entities, attributes, and relationships.
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Model Loaders
How graphical diagrams are ingested and parsed into internal metadata.
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Generators
Standards for generating OpenAPI 3.0, GraphQL, Protobuf, and Markdown outputs.
Frequently Asked Questions
Why can't I just use Generative AI to design my APIs?
While Generative AI can assist in drafting initial snippets, enterprise API generation requires 100% deterministic precision, strict governance, and repeatable standards.
- Zero Hallucinations: Generative models can introduce subtle field mismatches or non-standard syntax. MTT guarantees exact schema compliance every time.
- Single Source of Truth: Updating a core visual model automatically propagates breaking changes cleanly across OpenAPI, GraphQL, Protobuf, and documentation.
- Governance & Compliance: Architectural rules are built directly into MTT generators rather than relying on prompt engineering.
How does MTT integrate with existing GitHub repositories?
MTT uses standard repository structures for models and configs. You can execute transformations on local file stores or automate artifact generation directly within GitHub Actions pipelines.
Ready to run a transformation?
Return to the main MTT Manager web application to select your local or remote project and execute transforms.