Function Schemas and Typed Interfaces

Function schemas are the contract language between your LLM and its tools. A well-designed schema tells the model exactly what parameters a function expects, what types they must be, and what each parameter does—enabling accurate tool invocations and reducing runtime errors. This series teaches you to design schemas that guide models toward correct behavior, evolve them safely as your tools change, and generate type-safe code automatically.

Across these 10 articles, you'll learn the foundations of JSON Schema in the context of LLM tool calling, master type mapping from Python/TypeScript/Rust to structured definitions, write parameter descriptions that directly influence model accuracy, and implement versioning strategies so your APIs can evolve without breaking existing integrations. You'll see real patterns from production systems: optional and union types, validation pipelines, code generation from schemas, and how to wrap tools in type-safe abstractions that both humans and models can rely on.

Articles in this series

• What Is LLM Function Schema Design? Guide
• JSON Schema Fundamentals for LLM Tool Calls
• Mapping Language Types to JSON Schema
• How to Write Effective Parameter Descriptions
• Optional and Union Types in Function Schemas
• Versioning and Evolving Function Schemas
• Automatic Code Generation from Function Schemas
• Building Type-Safe Tool Wrappers
• Validation and Error Handling in Schemas
• Real-World Function Schema Design Patterns