JSON Query Tool (jq-like)

The JSON Query Tool (jq-like) is a free online tool that query and transform json data using jq-like syntax. extract, filter, and reshape with pipes, selects, maps, sort_by, unique, flatten, and more.. It runs entirely in your browser with no installation or sign-up needed.

Dot access (.key), array iteration (.[], .[0]), slicing (.[0:3]), pipes (|), select filters, map, sort_by, unique, flatten, keys, values, length, first, last.

No — it covers the most-used patterns. Full jq has hundreds of built-ins; this tool handles the 18 operations that cover 90% of common queries.

Yes. All processing in your browser. No data sent to any server.

Yes, the JSON Query Tool (jq-like) is 100% free with no registration, no hidden fees, and no usage limits. All processing happens locally in your browser, ensuring complete privacy.

Absolutely. The JSON Query Tool (jq-like) processes everything client-side in your browser. No data is uploaded to or stored on any server. Your content remains private on your device at all times.

Yes, the JSON Query Tool (jq-like) is fully responsive and works on smartphones and tablets. You can use it on any device with a modern web browser -- no app download required.

No account or registration is needed. Simply open the JSON Query Tool (jq-like) in your browser and start using it immediately. There are no sign-up walls or usage restrictions.

Simply enter your input in the provided field, adjust any settings to your preference, and the tool will process it instantly. You can then copy the result to your clipboard or download it.

The JSON Query Tool (jq-like) works in all modern browsers including Chrome, Firefox, Safari, Edge, and Opera. For the best experience, use the latest version of your preferred browser.

The pipe passes the output of one step into the next, so a query reads left to right like a small assembly line. In ".users | select(.active == true) | map(.name)", the first step pulls the users array, the second keeps only the items where active is true, and the third projects just the name out of each remaining record. Each stage operates on whatever the previous stage produced, which is why order matters: you usually narrow down to an array first, then filter, then map or sort. Without the pipe you would be limited to a single access expression. Chaining lets you turn a deeply nested API response into exactly the values you want in one line. Type a piped query in the bar above and the result updates live as you build each stage.

Use map with a dot path to project one field out of each element. For a list of users, ".users | map(.email)" returns a flat array of just the email values, dropping every other field. This is the fastest way to turn a verbose API list into a single column you can scan, copy, or paste into a spreadsheet. If you only want the unique values, chain unique onto the end: ".users | map(.role) | unique" collapses repeats so you see each distinct role once. You can also reach nested fields, like map(.address.city), as long as the path exists on each item. The result panel reports the element count, so you can confirm you got one value per record. Paste your array above and run a map query to pull out the column you need.

Most query errors come from applying an operation to the wrong kind of value. Slicing with .[0:3], indexing with .[0], and helpers like unique, flatten, and sort_by all expect an array, so pointing them at an object or a string raises a type mismatch instead of guessing. A common cause is forgetting a step: ".[0]" fails on a document whose top level is an object, whereas ".users[0]" works because .users is the array. The tool shows a plain-language error explaining what went wrong rather than failing silently, which makes it easy to spot that you need to drill into a key first. Check that the value at that point is actually a list, often by running just the path before the failing operation. Adjust the path in the query bar above and the result re-runs instantly so you can confirm the fix.

Use select with a comparison inside the parentheses to keep only the items you want. ".users | select(.role == "admin")" returns just the admin accounts, while ".users | select(.age >= 18)" keeps everyone of age. The supported comparisons are ==, !=, >, <, >=, and <=, with the ordered operators (>, <, >=, <=) meant for numbers and == or != working on strings, numbers, and booleans alike. Because select runs over each element of an array, you normally pipe an array into it first. Combine it with map to filter then project in one pass, like ".users | select(.active == true) | map(.name)". The result panel shows how many items survived the filter, a quick sanity check that it trimmed the set the way you expected. Try a select query above against your own data or the built-in sample.

To count, pipe an array into length: ".users | length" returns the number of users, and on a string or object it returns the character or key count instead. To grab endpoints without knowing the size, use first and last, as in ".metadata.tags | last", or index from the end with a negative number like .[-1], which the tool resolves relative to the array's length. These are handy for spot checks, such as counting records before and after a filter to see how many a select removed, or reading the most recent entry in a log-style array. The result panel also reports the output type and element count alongside the byte size, giving you a second confirmation of the number. Load your JSON above and pipe it into length, first, or last to read the counts you need.