JSON to TSV Converter

The JSON to TSV Converter takes an array of objects and renders it as tab-separated rows: a header line built from the keys, then one line per record. Tabs are the separator most spreadsheets and database import tools recognize without a configuration dialog, so a block of TSV usually drops straight into Excel, Google Sheets, or Numbers when pasted into a cell, and loads cleanly through psql, MySQL, or a bulk-load step that expects tab-delimited input.

It fits the moment when data arrives as JSON but the next tool wants a table. Typical cases: pulling a JSON response from an API or browser console and pasting the records into a sheet for a quick scan, handing structured output to a colleague who works in spreadsheets rather than code, or staging rows for a legacy or internal system that reads TSV but not JSON. Because the whole conversion runs in your browser, you can paste production or internal data without it leaving the tab.

It is a formatting step, not an analysis tool. It reshapes records you already have into a flat table; it does not query, filter, aggregate, or validate the meaning of the data. If your end goal is comma-delimited output or an actual .xlsx workbook, the sibling JSON to CSV and JSON to XLSX tools are the better starting points.

The flow is direct: paste your JSON (or load a file) into the input, and the converter parses it and writes the TSV result, which you can copy to the clipboard or download as a .tsv file. The expected shape is a top-level JSON array of objects, for example [ { "name": "FormatOS", "count": 128 }, ... ]. A single object on its own is also accepted and produces one data row.

Headers are derived from the union of keys across all objects, so records do not have to share an identical shape: a key present in some objects but absent in others still becomes a column, and rows missing that key get an empty cell in its place. Values that are themselves objects or arrays are written as their JSON text inside the cell rather than being expanded into more columns. Cells that contain a tab, quote, or line break are wrapped in double quotes with internal quotes doubled, so the row structure stays intact.

What it does not do is flatten nested structures into separate columns, infer or reorder a schema, or coerce types beyond turning each value into text. Numbers, booleans, and null are rendered as plain strings. There are no delimiter or quoting options to set, since the output is fixed to tab-separated; the tool's job is one specific transformation, done predictably.

The most common failure is feeding it something other than an array of objects. A top-level array of plain values (strings or numbers), an object whose values are arrays, or deeply nested JSON will not produce the table you might expect: only top-level entries that are plain objects become rows, and anything else is skipped or collapsed into a single JSON-text cell. If the output looks empty or has one cluttered column, check that the structure is a flat array of records.

Invalid JSON stops the conversion outright, so a trailing comma, an unquoted key, single quotes instead of double, or a fragment copied mid-response will fail to parse rather than partially convert. Nested fields are another quiet pitfall: an address object or a tags array lands in the cell as raw JSON, which is faithful but rarely what a spreadsheet user wants to read, so flatten those upstream if you need them as their own columns.

Watch the edges of TSV itself. Source values that already contain tab characters or newlines get quoted to preserve the row, and not every downstream importer interprets that quoting the same way, so spot-check such fields. Key order follows first appearance across the records, and a missing key produces a blank cell rather than an error, which can make a sparse dataset look misaligned at a glance.

Open the result and confirm the header row lists the columns you expected and that the row count matches the number of records you put in. Scan a few rows to verify values sit under the right headers, that nested fields rendered the way you intended, and that empty cells correspond to genuinely missing keys rather than a parsing slip. If you are pasting into a spreadsheet, paste a sample first and confirm the columns split on tabs rather than collapsing into one cell.

Pay attention to fields that carry special characters, leading zeros, long numeric IDs, dates, or currency, since the converter passes values through as text and the receiving application may reformat or truncate them on import. Re-paste a couple of those specific cells and compare them against the source JSON.

For anything official, financial, or legal, treat the output as a draft and verify it manually against the source. This tool reformats data without checking its accuracy or completeness, so a person should confirm totals, identifiers, and record counts before the file is filed, submitted, or used to make a decision.