RunToolRunJSON to Excel
Convert JSON files to Excel format.
How to Convert JSON to Excel
Paste your JSON array
Drop or paste an array of objects — API responses, exports, logs.
A workbook is built locally
Keys become column headers and objects become rows in a real .xlsx file, generated in your browser.
Download the Excel file
Open it directly in Excel, Google Sheets or LibreOffice — no import wizard needed.
Turning records into rows
An array of objects maps naturally onto a sheet: each object is a row, each key a column. That is the shape most API responses and database exports arrive in, which is why this conversion is usually straightforward.
What is not straightforward is everything below the top level. Nested objects have to become dotted column names, and arrays have to become either several columns, a joined string, or repeated rows — a decision the data cannot make for you.
Records with different keys
A spreadsheet needs one header row covering every record, so the columns are the union of all keys found. Optional fields leave blanks, and a blank cannot distinguish between a key that was absent, a key that was null, and a key holding an empty string.
That ambiguity is worth knowing before anyone analyses the sheet, because 'empty' will be counted as one thing when it may be three.
Types survive, then Excel intervenes
JSON has real types, and a proper workbook can preserve them: numbers as numbers, booleans as booleans, text as text. That is a genuine advantage over routing the data through CSV, where everything becomes text and has to be guessed back.
The exception is the familiar one. Long identifiers, codes with leading zeros and date-shaped strings need to stay text, and a conversion that treats them as numbers loses information the moment it does so.
Deeply nested data does not belong in a sheet
Two levels of nesting produce readable column names. Four produce names nobody can parse and a sheet nobody can use. At that depth the data is telling you it is not tabular, and flattening it is fighting its shape.
The better approach for genuinely hierarchical data is to extract the specific view you need — one entity type, with the fields that matter — rather than trying to represent the whole structure at once.
Large exports
Spreadsheets have row limits and, well before those, practical limits: a sheet with hundreds of thousands of rows is slow to open and unpleasant to work in. If the data is that large, it belongs in a database rather than a workbook.
Conversion happens in your browser, so a very large JSON file is bounded by your device's memory. Converting the subset you need is faster and easier to check than converting everything.
Why this is a common step
APIs speak JSON and people work in spreadsheets. Converting is how a developer hands data to a colleague who needs to filter, sort and pivot it without writing code, and it is one of the most frequent small tasks in any office with an API in it.
None of the data is uploaded here, which matters because API exports routinely contain personal data and access tokens that nobody remembers are in the file.