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A JSON or XML validator checks whether data can be parsed; a schema-aware validator can also check whether it follows a defined structure. For a quick check, use an online tool only with public or synthetic data. For private payloads, validate locally. For production or exchanged data, validate against the exact JSON Schema, DTD, or XSD expected by the receiving system.
Choose the right kind of validation
“JSON and XML validator” describes a category of tools, not one universal standard or product. First decide what you need checked:
| Check | JSON | XML |
|---|---|---|
| Syntax | Can a JSON parser read the text? | Is the document well-formed XML? |
| Schema or contract | Does it match a JSON Schema? | Does it satisfy a DTD, XSD, Relax NG schema, or Schematron rules? |
| Application meaning | Are the values acceptable to the application? | Are the values acceptable to the application? |
A syntax check alone does not establish that data is complete, meaningful, safe, or compatible with another system. For JSON, the syntax is defined by RFC 8259. XML distinguishes a document that is well-formed from one that is also valid against additional constraints, as explained in the W3C XML specification.
Recommended Free Tools
Quick recommendation
- One-off check of non-sensitive JSON: an online parser such as JSONLint can validate and format JSON.
- JSON contract: use a JSON Schema validator that supports the draft your schema declares.
- One-off XML check: use an XML parser for well-formedness; supply a DTD or XSD if you need schema validation.
- Private data, automation, or repeated checks: validate locally with a command-line tool, application library, or build/CI step.
- Complex XML authoring: an XML editor may help with schema-aware editing, namespaces, and related workflows; it is unnecessary for an occasional syntax check.
The W3C Markup Validation Service is useful for web-document validation and certain XML/DTD workflows, but it is not a universal validator for every XML schema language. See its scope and limitations before choosing it for a particular format.
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Validate JSON
What valid JSON looks like
Standard JSON uses double quotes around object keys and strings, commas between members or array items, and lowercase true, false, and null. Comments, single-quoted strings, and trailing commas are not standard JSON. Numbers must follow JSON number syntax; constructs such as NaN, Infinity, and hexadecimal literals are not valid JSON numbers. Under RFC 8259, a JSON document is a serialized value; it is not restricted to an object at the top level.
{
"name": "Ada",
"age": 36,
"active": true,
"tags": ["developer", "researcher"],
"middleName": null
}For example, this is not standard JSON:
{
name: 'Ada',
"age": 036,
"active": True,
}The key is unquoted, the string uses single quotes, the number uses a non-portable leading-zero form, True has the wrong capitalization, and the object ends with a trailing comma. JSON-like formats such as JSON5, JSONC, or JavaScript object literals may allow some of these features; identify the actual format before trying to validate or “repair” a file.
Check JSON in a browser
For non-sensitive input, paste or upload the document to a validator, run its check, and inspect the first reported error. Correct the issue and validate again. Format or pretty-print only after parsing succeeds: formatting changes layout, not the data contract. JSONLint describes its site as an online JSON editor, validator, and formatter. Its separate JSON Schema tool states that it uses Ajv and supports Draft 7 by default; that is a claim about that tool, not all JSON validators.
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Check JSON locally
These commands parse a file without sending it to an online service:
# Python: parse and pretty-print to standard output
python -m json.tool data.json
# jq: parse the input and emit no result
jq empty data.json
# Node.js: parse the file as JSON
node -e "const fs=require('fs'); JSON.parse(fs.readFileSync(process.argv[1], 'utf8')); console.log('valid JSON')" data.jsonA successful parse confirms syntax only. In shell scripts or CI, check the command’s exit status; do not treat formatted output as schema validation.
Validate against JSON Schema
A JSON Schema can define required properties, types, value ranges, allowed strings, array contents, nested object shapes, and other constraints. For example, it can reject {"age":"thirty-six"} when age must be an integer. Before choosing a validator, check the schema’s $schema declaration and confirm the implementation supports that draft. Also check how it handles format keywords, unknown properties, custom keywords, remote references, and strictness: implementations can differ.
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Even a successful schema check does not settle every application rule. A schema might allow a negative amount while the business process does not. Encode such rules explicitly or validate them in application logic.
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Well-formedness comes first
A well-formed XML document has one root element, properly nested and matching start and end tags, quoted attribute values, and correctly escaped reserved characters such as & and <. XML names are case-sensitive.
<person>
<name>Ada</name>
<active>true</active>
</person>In this broken example, <name> is not closed before the root element closes:
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<person>
<name>Ada</person>Schema validity is a separate check
A document can be well-formed but fail its schema. For example, an XML parser may accept an <age>unknown</age> element, while an XSD that requires an integer rejects it. A schema can also require a particular element order, namespace, attribute, or child element. XML schema options include DTD, XSD, Relax NG, and Schematron; use the one the receiving system actually specifies.
Check XML locally with xmllint
If xmllint is installed, these common commands check basic structure or validate against an XSD:
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# Check well-formedness
xmllint --noout data.xml
# Validate against an XSD
xmllint --noout --schema schema.xsd data.xml
# Validate against a DTD declared by the document
xmllint --noout --valid data.xmlAvailability and behavior depend on the installed tool and XML libraries. For regulated or industry-specific documents, confirm parser version, entity settings, namespace behavior, and support for the schema features your document uses. For repeatable build validation, Apache Ant provides an xmlvalidate task; the Ant documentation describes its configuration.
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Watch XML namespaces
An element can look right on screen and still fail validation because it belongs to the wrong namespace. A namespace prefix is only an alias; what matters is the namespace URI associated with the element. When a validator reports an unexpected element, compare namespace URIs as well as visible names.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common validation errors and fixes
| Message or symptom | Likely cause | What to inspect |
|---|---|---|
| JSON: “Unexpected token” | Wrong quote, comment, extra character, or malformed value | The reported character and the value immediately before it |
| JSON: “Expecting property name” | Unquoted key, broken object, or trailing comma | The start of the object member and the preceding comma |
| JSON: “Unexpected end of input” | Missing closing brace, bracket, or quote | Open structures and strings near the end of the file |
| JSON: “Invalid escape” | Backslash followed by an unsupported escape sequence | Escape sequences in the indicated string |
| JSON: error reported at the next key | A comma may be missing on the preceding member | The line before the reported location |
| XML: mismatched tag | Start and end tag names differ or nesting is broken | The element opened immediately before the error |
| XML: datatype or element-order failure | Well-formed document violates the schema | Expected type, order, required content, and namespace |
Parser locations often show where the tool noticed a problem, not where it began. If the indicated character looks valid, inspect the preceding line, delimiter, or open element.
Choosing a validator for a project
- Online validator: convenient for a quick check, visual error highlighting, or formatting when the data is public and no project-specific contract is involved.
- Local command-line tool: better for confidential input, offline use, scripting, CI, batches of files, and reproducible exit codes.
- Editor or IDE: useful for interactive work on large files, autocomplete, schema-aware editing, namespace navigation, XPath, or XSLT.
- Application library: appropriate when a service must reject malformed requests, return structured errors, and apply size, depth, time, or reference limits.
- Build integration: useful when every committed document must be checked consistently. Pin tool and dependency versions and test the schema-validation behavior the project relies on.
When comparing tools, prioritize exact schema support, privacy and data handling, automation, useful errors, performance on your file sizes, namespace and reference behavior, and maintenance. A tool that accepts a document under a different schema draft or parser configuration may not match the receiving system.
Privacy and security
Do not paste access tokens, passwords, API keys, customer records, health or payment data, proprietary configuration, or production request bodies into an unknown online validator. A site may receive the submitted content; check its data-handling practices rather than assuming an online form is private. URL-based validation can also fetch remote content, so consider what the service will request and what may appear in browser history or logs. Use local validation for confidential material.
For untrusted XML, review how the parser handles external entities, DTDs, schema includes/imports, and remote references. Configure the parser to disable unsafe external resource resolution where appropriate. For production validation of either format, impose practical limits on input size, nesting depth, execution time, and resource use; schemas and regular expressions can also introduce costly work.
Important edge cases
- Duplicate JSON keys: RFC 8259 warns about interoperability when object names are not unique. Parsers may keep the first value, keep the last, or reject the object. Avoid duplicates when data crosses systems.
- Numeric precision: JSON syntax permits numbers, but runtimes differ in numeric range and precision. A syntactically valid value may be rounded or overflow in a consumer.
- Unicode and encoding: A successful structural parse does not guarantee that every downstream system handles characters, encoding, or normalization consistently. Check the requirements of the actual interchange format.
- Passing is not proof of quality: A validator checks the rules it knows. It does not establish business correctness, security, accessibility, or interoperability unless those checks are explicitly included. The W3C Validator makes this limitation clear for its own service.
For web and XML validation options, the W3C provides service documentation and guidance that discusses online and offline tools, editors, IDEs, and batch validation. Choose by format and validation layer rather than assuming one “JSON and XML validator” checks everything.
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