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[ARTICLE · art-109951] src=dev.to ↗ pub= topic=developer-tools verified=true sentiment=· neutral

Convert JSON to MD for your agents

A developer released json-to-md, an open-source tool that converts JSON documents into deterministic, human-readable GitHub Flavored Markdown. The tool runs in the browser, Node, Go, and on the command line, with TypeScript and Go implementations producing byte-identical output. It aims to reduce token usage when feeding data to LLMs by replacing JSON punctuation with Markdown structure.

read1 min views2 publishedAug 25, 2026

Convert one JSON document into deterministic, human-readable GitHub Flavored Markdown.

Runs in the browser, in Node, in Go, and on the command line.

Output is a readable projection rather than a reversible serialization format. Every document begins with a # Results

heading (replace or omit it via the heading

option), leaves values bare unless showTypes: true

opts into type annotations — 42 *(integer)*

— and uses canonical spacing: LF endings, one blank line between blocks, no trailing spaces, one final newline.

If you feed JSON into an LLM, you pay for its punctuation. Every { , }

, "

,

:

, and ,

is tokens spent on structure the model does not need to read the

data. Converting the same document to Markdown headings and lists is measurably

cheaper and easier for models to follow:

Convert one JSON document into deterministic, human-readable GitHub Flavored Markdown.

The same conversion runs in the browser, in Node, in Go, and on the command line. The TypeScript and Go implementations produce byte-identical output, enforced by a shared corpus/ and a cross-implementation fuzz gate in CI. Output is a readable

# Results

heading (replace or omit it via the heading

option), leaves values bare unless showTypes: true

opts into type annotations — 42 *(integer)*

— and uses canonical spacing: LF endings, one blank line between blocks, no trailing spaces, one final newline.If you feed JSON into an LLM, you pay for its punctuation. Every {

, }

, "

:

, and ,

is tokens spent on structure the model does not need to read the data. Converting the same document to…

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