What schema shapes does it accept?

A plain JSON Schema object with a properties map, or a full LLM tool definition wrapper from OpenAI (function.parameters) or Anthropic (input_schema). The tool detects the wrapper and unwraps to the underlying schema automatically.

Does it handle nested objects and arrays?

Yes. Nested object properties are documented with dotted paths, and array item types are shown including the element schema. This keeps deeply structured tool inputs readable without losing the hierarchy.

Why document tool schemas at all?

When you expose function-calling tools to an LLM or to other developers, the parameter descriptions are part of the contract the model reads. Clear, human-readable docs catch missing descriptions and ambiguous types that would otherwise cause the model to call the tool incorrectly.

Is anything sent to a server?

No. Parsing and rendering happen entirely in your browser, so you can document private internal tool schemas without exposing them. Nothing leaves the page.

JSON Schema → Human-Readable Docs

Turn a JSON Schema into docs people can actually read

A JSON Schema is precise but not pleasant to read, and for LLM tool definitions the parameter descriptions double as the contract the model reads to decide how to call your tool. This generator converts a schema — including OpenAI and Anthropic tool wrappers — into a clean Markdown reference table with types, required flags, allowed values, and defaults.

How it works

You paste a JSON Schema. If it is wrapped in a tool definition (OpenAI’s function.parameters or Anthropic’s input_schema), the tool unwraps it automatically. It then walks the properties map, recursing into nested objects and array item schemas, and emits a Markdown table. Each row shows the parameter name (with a dotted path for nested fields), its type, whether it is required, any enum values or default, and the description. Invalid JSON is reported with the parse error so you can fix it fast.

Tips for better tool docs

Write a description for every parameter. The model uses these to decide argument values; a blank description is a common cause of wrong tool calls.
Use enum for closed sets. Documenting allowed values explicitly beats a free-text string the model has to guess at.
Mark required fields accurately. The generated table surfaces gaps where a field is used but not listed in required.
Regenerate the docs whenever the schema changes so your reference never drifts from the actual contract.