JSON to Excel
Paste any JSON array or object and get a clean, structured XLSX file back — arrays become rows, keys become column headers, and nested objects are flattened automatically. No reformatting required. Free to start.
How it works
Paste your JSON
Copy your JSON array or object from an API response, database export, or any file and paste it directly into the input field. No special formatting or preparation needed.
Describe your preferences
Specify optional instructions — such as which fields to include, how to label columns, or how to handle nested keys — so the output matches exactly what you need in the spreadsheet.
The AI agent structures your data
The agent parses your JSON, maps every key to a column header, flattens nested objects using dot notation, and arranges each array item as a numbered row in the spreadsheet.
Download your Excel file
Receive a clean, ready-to-use XLSX file you can open in Excel, Google Sheets, or any spreadsheet tool — sorted, labeled, and free of the manual reformatting that would otherwise take hours.
Who is this for
Developers & API builders
Quickly turn API responses or database JSON exports into spreadsheets for sharing with non-technical teammates, stakeholders, or clients who work in Excel rather than code.
Data analysts & business teams
Skip manual copy-paste reformatting when working with JSON-based data sources. Get a clean, pivot-ready Excel table in seconds so you can jump straight into analysis.
Operations & project managers
Convert JSON reports, inventory exports, or CRM data dumps into structured spreadsheets for tracking, reporting, or sharing — without needing a developer to write a conversion script.
Six prompt-engineering tips that move the needle
Small changes in how you write a prompt make the biggest difference in output.
Specify which fields to keep
If your JSON has many keys you don't need, list only the ones you want as columns. For example: 'Include only id, name, and email columns.' This keeps your Excel sheet focused and readable.
Give columns readable names
Tell the agent to rename cryptic keys into plain language. For instance: 'Rename usr_id to User ID and ts to Timestamp.' Column headers in the final spreadsheet will match exactly what you specify.
Control nesting depth
If your JSON has deeply nested objects, indicate how far to flatten. 'Flatten only one level deep' keeps the output manageable, while 'fully flatten all nested keys' exposes every value as its own column.
Request a specific row sort order
Add a sorting instruction such as 'Sort rows by date descending' or 'Order by total_amount from highest to lowest' so the spreadsheet arrives pre-sorted and ready to review.
Handle arrays within objects explicitly
If a field contains a nested array (e.g., a list of tags per record), specify what to do: 'Join array values as a comma-separated string in one cell' or 'Expand into multiple rows — one per tag.'
Flag missing or null values
Tell the agent how to represent nulls: 'Replace null values with N/A' or 'Leave null cells blank.' This prevents ambiguous empty cells that could skew formulas or filters later in Excel.
What to expect
Most JSON arrays with up to 500 rows and 2–3 levels of nesting convert in under 30 seconds, producing a structured XLSX with accurate column headers. Results may vary for very large datasets or JSON with inconsistent key schemas across objects.
Example: A 120-row JSON export from a Shopify-style orders API — each order containing a nested 'customer' object and a 'line_items' array — gets flattened into a single Excel sheet with columns like customer.name, customer.email, and line_items joined as comma-separated product names, ready for monthly sales reporting.
Good to know
- JSON arrays nested inside object values (e.g., a 'tags' field containing ['red','blue']) require explicit instructions for how to expand or join them — the default behavior may not match your intended layout.
- Extremely large JSON files (thousands of rows with 10+ nesting levels) can produce very wide spreadsheets with dozens of dot-notated columns that may need manual grouping or cleanup before they are practical to use.
- The tool converts structure and content but does not apply Excel formulas, conditional formatting, or data validation rules — any formula-based logic must be added manually after the file is generated.
Frequently asked questions
What JSON structures does this tool handle best?
It works best with arrays of objects — the most common JSON export format. Each object in the array becomes one row, and every unique key across all objects becomes a column. Single objects and nested structures are also supported.
How are nested objects handled in the Excel output?
Nested objects are flattened using dot notation. For example, a field like 'address.city' becomes its own column header, keeping all data in a single, readable sheet rather than splitting across multiple tabs.
What happens if different objects in my array have different keys?
The tool creates a union of all keys found across every object as column headers. Rows that lack a particular key simply show an empty cell in that column, so no data is lost.
Is there a size limit on the JSON I can convert?
Very large JSON files — typically those over a few thousand rows or with deeply nested structures spanning 5+ levels — may produce slower results or require manual cleanup. Splitting large files before converting usually gives cleaner output.
Will the Excel output preserve number and date types, or convert everything to text?
Numeric values are typically preserved as numbers in the XLSX output. Dates in ISO 8601 format (e.g., '2024-03-15') are usually recognized and formatted correctly, though custom date strings may come through as text.
Can I convert JSON that I exported from a database, API, or tool like MongoDB or Postman?
Yes — JSON exports from REST APIs, MongoDB, Firebase, Postman, and similar sources are common use cases. As long as the data follows standard JSON syntax, the conversion process treats it the same way regardless of origin.
Do I need to clean up my JSON before pasting it in?
Minor formatting issues — trailing commas, inconsistent spacing — are often handled gracefully, but the JSON must be structurally valid. Running it through a free JSON validator first is a good habit if you are unsure about the source data.
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