Data types
Numbers, booleans, dates and nulls can become plain text when moved into loosely typed formats.
JSON and API Tools
JSONPath Explorer Online Free
Test JSONPath queries online. Paste JSON, enter a JSON path and extract values from nested objects, arrays, API responses and structured data.
JSONPath query workspace
Explore direct properties, array indexes and wildcard selections without uploading JSON.
Matches
2
Input size
210
Supported
Dot · index · wildcard
Input JSON
Matching value(s)
[ "Book One", "Book Two" ]
This lightweight explorer intentionally supports common browser-safe JSONPath patterns: property access, numeric indexes and * wildcards. Advanced filters and recursive descent are not evaluated.
JSON query guide
Test JSONPath expressions against real structures
JSONPath provides a compact way to select values from nested JSON. Exact syntax and feature support can vary between libraries, so test expressions against representative payloads and document the JSONPath implementation used by your application or test framework.
✓ Browser processing
✓ No account required
✓ Free to use
✓ Developer friendly
Step-by-step
A reliable workflow
1
Paste valid representative JSON.
2
Start with a simple root or direct-property path.
3
Add array indexes, wildcards or filters gradually.
4
Verify every returned value before reusing the expression.
Data transformation
Preserve structure, types and business meaning
Converting structured data is not only a formatting task. Arrays, nested objects, dates, numbers, null values and duplicate keys may map differently between JSON, CSV, XML, YAML and spreadsheets. A successful export should preserve the meaning needed by the destination system, not merely create a file that opens.
Nested structure
Tabular formats require flattening, multiple sheets or repeated rows to represent nested arrays and objects.
Schema expectations
Valid syntax does not guarantee that required fields, allowed values or relationships are correct.
Character encoding
UTF-8 handling is essential for names, symbols and multilingual data.
What the operation can change
- Flattening can turn nested paths into column names.
- CSV cannot natively preserve objects, arrays or strong data types.
- Spreadsheet software may reinterpret identifiers, dates and leading zeros.
- Round trips between formats may not reproduce the original structure exactly.
Domain-specific verification
- Compare record counts and required field counts.
- Inspect leading zeros, large numbers, dates, nulls and Unicode text.
- Validate against a schema or import a small sample into the destination system.
- Keep the original source file and document any flattening convention used.
Before processing
Input checks that prevent bad conversions
- 01Start with valid JSON and identify the exact object or array that contains the target values.
- 01Use a reduced sample that still contains the nesting and edge cases being queried.
- 01Check whether property names contain spaces, dots or special characters that require bracket notation.
After processing
Verify the result before integration
- ✓Confirm the expression against multiple records, including missing and null fields.
- ✓Check whether the result is a scalar, object, array or empty match before integration.
- ✓Test the expression in the same JSONPath library used by the target application when possible.
Troubleshooting
Common conversion failures
The expression returns no result
Check the root symbol, array indexes, property spelling and whether the field exists in every record.
Different tools return different results
JSONPath implementations vary in filter, script and function support; verify the target library syntax.
Only the first item matches
Use wildcard or recursive selection deliberately and review the resulting array shape.
Practical use cases
API testing
Assert values inside nested responses.
Automation
Extract IDs, statuses and totals from payloads.
Monitoring
Select fields from webhook or event data.
Data analysis
Explore arrays and deeply nested properties.
Common mistakes
- ⚠Assuming every JSONPath library supports identical filters
- ⚠Using the wrong array index
- ⚠Testing against only one payload shape
- ⚠Ignoring fields that may be absent or null
Professional tips
- ✓Build paths one segment at a time
- ✓Test empty arrays and missing properties
- ✓Prefer clear direct paths over unnecessarily broad recursive queries
- ✓Record the expected number and type of results
Continue the workflow
Frequently asked questions
Is JSONPath a single universal standard?+
There is an IETF standard, but existing libraries may differ in legacy syntax and optional features. Check your runtime implementation.
What does the dollar sign represent?+
It commonly represents the root JSON value.
How are arrays queried?+
Arrays are commonly accessed with zero-based indexes or wildcards, depending on the implementation.
Why does an expression work here but not in my framework?+
The framework may use a different JSONPath dialect or may not implement filters and operators in the same way.
Can JSONPath modify JSON?+
JSONPath is primarily a query language. Modification depends on the surrounding library.
How should missing values be handled?+
Design the consumer to handle no match, null and type variations explicitly.
Is my data uploaded to a server?+
The supported FormatForge data tools process input in your browser. Avoid pasting secrets, production credentials or personal data into any online utility.
Can I use the tool on mobile?+
Yes. For large files, deeply nested data or long configuration files, a desktop browser normally provides more memory and a better editing area.
What should I do before using converted output?+
Validate the result, compare representative records and test it in a non-production environment before using it in an application, pipeline or deployment.
Why can large files take longer?+
Parsing, flattening, validating and rendering large nested structures uses your device memory and processor.