Format Detection Tool

Convert CSV rows with headers into a clean JSON array.

Convert a JSON array of objects into CSV with generated headers.

Validate CSV syntax and structure — checks column consistency, unclosed quotes, and empty headers.

Validate JSON syntax and surface parse errors.

Pretty-print JSON with consistent indentation.

Minify JSON by removing unnecessary whitespace.

Compare two JSON documents and highlight differences.

Sort object keys alphabetically for stable JSON output.

Escape JSON into a quoted string literal for embedding.

Unescape a quoted JSON string back into readable JSON or plain text.

Flatten nested JSON objects into single-level key paths.

Rebuild nested JSON from flattened path-based keys.

Generate a draft JSON Schema from sample JSON input.

Validate JSON data against a provided JSON Schema.

Evaluate JSONPath expressions against JSON input.

Run jq-like query expressions to extract or transform JSON data.

Convert JSON objects or arrays into well-formed XML.

Parse XML into a formatted JSON representation, preserving nested elements and attributes.

Convert JSON objects and arrays into clean, human-readable YAML.

Parse YAML and convert it to formatted JSON.

Validate YAML syntax and report the parsed type and top-level keys on success.

Check XML well-formedness and report the root element and any parse errors with line numbers.

Merge multiple JSON documents into one output object.

Split large JSON arrays into smaller chunks.

Rename one or more keys across JSON objects.

Find and replace values in JSON content.

Filter JSON records using key/value conditions.

Evaluate RFC 6901 JSON Pointer expressions against JSON input.

Compare two XML documents and highlight structural differences.

Evaluate XPath expressions against XML input.

Run a clearly labeled practical XQuery subset for XML exploration: direct XPath, simple for/let expressions, count(), and string().

Validate XML against a provided XSD schema.

Compare two YAML documents and report differences.

Lint YAML style and structure with stricter quality rules.

Evaluate path expressions against YAML input.

Run query expressions to extract or transform YAML data.

Lint JSON syntax and style with stricter practical checks.

Generate RFC 6902-style patch operations from left/right JSON values.

Compute row/column stats and numeric summaries for CSV data.

Validate YAML content against a JSON Schema.

Apply RFC 6902-style patch operations to JSON data.

Analyze JSON size and structural complexity metrics.

Escape XML special characters for safe embedding.

Decode escaped XML entities back to plain characters.

Convert JSON row objects into SQL INSERT statements.

Convert JSON row objects into an XLSX base64 output envelope.

Flatten nested XML into a path-based key representation.

Render JSON as an interactive collapsible tree with type labels and copyable paths.

Render a JSON array of objects as a sortable, searchable table for fast inspection.

Profile CSV columns with inferred type, emptiness, uniqueness, top values, and numeric percentiles.

Profile field-level type consistency for JSON arrays, including null-rate and unique count metrics.

Audit JSON Schema complexity with required/optional coverage, depth, and missing description checks.

Analyze XML structure with element counts, tag frequency, depth, attribute totals, and namespace usage.

Analyze YAML structure for key depth, type distribution, array sizing, and anchor/alias usage.

Detect, pretty-format, and analyze API responses into a JSON report with HTTP metadata and formatted payload previews.

Compare JSON, XML, YAML, CSV, text, or SQL payloads from one unified entry point.

Run JSONPath, XPath, XQuery-subset, YAMLPath, and JSONata queries through one tool.

Auto-detect sources and convert across supported JSON, XML, YAML, CSV, and Excel pairs, including structured JSON input and Excel base64 envelopes.

Generate deterministic mock records for JSON/CSV/XML/YAML testing.

Project and rename fields from a JSON array of objects. Select which columns to keep and optionally rename them.

Group a JSON array of objects by a specified field. Output as a keyed object or as an array of groups.

Generate mock CSV with configurable columns including uuid, email, integer, float, date, boolean, enum, and name.

Generate a valid JSON template from JSON Schema using defaults, examples, enums, and type placeholders.

Generate UUID or sequential IDs in JSON array, newline, or CSV formats.

Generate timestamps in multiple formats for offsets from an anchor date.

Identify data quality issues in CSV: missing values, numeric outliers (IQR), type inconsistencies, and duplicate key rows.

Given a regex pattern, generate N example strings that match it. Useful for structured IDs, codes, and format-specific mock values.

Normalize CSV by parsing and re-emitting with consistent quoting and proper cell escaping.

Pretty-print JSON with consistent indentation.

Minify JSON by removing unnecessary whitespace.

Sort object keys alphabetically for stable JSON output.

Normalize YAML by parsing and re-emitting with consistent 2-space indentation and block style.

Pretty-print XML with consistent 2-space indentation for improved readability.

Minify XML by removing whitespace and indentation.

Sort YAML mapping keys alphabetically, recursively.

Sort CSV rows by one or more columns.

Compress YAML into a compact representation.

Sort XML element structures for deterministic ordering.

Detect, pretty-format, and analyze API responses into a JSON report with HTTP metadata and formatted payload previews.