The JSON structure analyzer validates your JSON and scores it across 5 dimensions — validity, nesting depth, size distribution, type diversity, and key naming conventions. Get a structure quality report with a collapsible tree view and type breakdown chart.
Invalid JSON
Structure Score
Structure Dimensions
Type Distribution
Structure Tree
How we score
Validity (25%): JSON.parse() validation — any syntax error drops this dimension to 0.
Depth (25%): Maximum nesting depth. Under 5 levels scores well; deeper structures are harder to query.
Size Distribution (20%): Total key count and array balance. Very large flat objects suggest restructuring opportunities.
Type Diversity (15%): Mix of strings, numbers, booleans, arrays, objects, and nulls. Diverse types indicate expressive data modeling.
Key Naming (15%): Checks for consistent camelCase, snake_case, or kebab-case. Mixed conventions score lower.
How to Analyze JSON Structure
Good JSON structure makes APIs intuitive, reduces client-side complexity, and catches data modeling issues before they reach production. The JSON structure analyzer gives you a quality score across the dimensions that matter most for maintainable APIs.
Step 1: Paste and validate
Copy your JSON response, configuration file, or data payload. If the JSON is invalid, the analyzer shows the parse error with position information. Common mistakes: trailing commas (remove the last comma in arrays/objects), single quotes (use double quotes), or unquoted keys (all JSON keys must be double-quoted strings).
Step 2: Check depth and structure
The tree view shows your JSON structure with type indicators. A depth of 5+ levels usually means the data model can be flattened. If a value at response.data.user.profile.preferences.theme is accessed often, consider surfacing frequently-accessed fields closer to the root.
Step 3: Enforce naming conventions
Inconsistent key naming is a common API design issue. If your JSON mixes userId and user_name, clients must handle both conventions. Pick one style — camelCase for JavaScript-first APIs, snake_case for Python/Ruby APIs — and apply it consistently to all keys.
Reading the type distribution
The doughnut chart shows the breakdown of value types across your entire JSON. A mix of strings, numbers, booleans, and nested objects indicates expressive data modeling. An all-string JSON often means numbers and booleans were accidentally stringified during serialization — a common bug that the analyzer will flag.
FAQ
What is a good JSON nesting depth?
A depth of 3-5 levels is generally manageable. Beyond 5 levels, JSON becomes hard to navigate and query. Deeply nested JSON (8+ levels) often signals a data modeling issue — consider flattening relationships or using references. REST API responses typically stay under 4 levels deep.
What naming convention should JSON keys use?
camelCase is the most common in JavaScript APIs (userId, firstName). snake_case is common in Python/Ruby APIs (user_id, first_name). kebab-case (user-id) works in JSON but cannot be used as JavaScript identifiers without bracket notation. The most important rule is consistency — don't mix conventions in the same API.
How do I validate JSON format?
JSON requires double quotes around strings and keys, no trailing commas, no comments, and specific value types (string, number, boolean, null, array, object). This analyzer uses JSON.parse() to validate — any syntax error shows the position of the problem. Common mistakes: single quotes, missing quotes on keys, trailing commas.
What types are valid in JSON?
JSON supports 6 types: string (double-quoted text), number (integer or float), boolean (true/false), null, array (ordered list), and object (key-value map). JavaScript-specific types like undefined, Date, and functions are NOT valid JSON — they become null or get omitted when serialized.
Is this tool free and private?
Yes, completely free. Your JSON is analyzed locally in your browser using JSON.parse() — no data is sent to any server.
What does a high key count mean?
A JSON object with 30+ keys is a potential design smell — it may be doing too much. Consider splitting into nested sub-objects grouped by concept. However, flat objects with many keys are sometimes intentional in data exports or configuration files where nesting would add complexity without clarity.