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24KLabs Data Format Validator & Linter
x402
24KLabs Data Format Validator & Linter is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Validates and lints JSON, YAML, CSV, or XML input, returning a valid flag, the first error location (line/column), and a normalized re-serialized copy when valid.
Price / call$0.002
Success—
StatusUnknown
How this service works
Validate and lint JSON, YAML, CSV, or XML. Returns a valid flag, the line/column of the first error when invalid, and a normalized (re-serialized) copy when valid.
Output
Returns a JSON object containing: a boolean 'valid' field, a 'line' and 'column' integer indicating the position of the first error when invalid (null when valid), an optional 'error' message string describing the issue, and a 'normalized' string containing the re-serialized/canonicalized version of the input when valid.
Example use cases
CI pipeline config validation
“I have a YAML Kubernetes config file I'm about to deploy — can you validate it for me and tell me if there are any syntax errors? Here's the content: 'apiVersion: v1\nkind: Pod\nmetadata:\n name: my-pod\nspec:\n containers:\n - name: my-container\n image: nginx\n ports:\n - containerPort: 80\n'”
API response body sanity check
“My backend just returned this JSON and something looks off — can you validate it and return the normalized version if it's valid, or tell me the exact line and column of the first error if it's not? Here it is: '{"status":"ok","data":{"id":42,"tags":["a","b"]}'”
CSV data file verification
“I got a CSV export from our analytics tool and I need to make sure it's well-formed before importing it — can you lint it and give me back a normalized copy if it passes? The content is: 'name,age,city\nAlice,30,NYC\nBob,25,LA'”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/data-format-validateWhen to prefer
Choose this endpoint when you need a quick, deterministic syntax check and normalization for structured data formats (JSON, YAML, CSV, XML) with precise error location reporting. It is ideal for CI/CD pipelines, pre-flight checks before sending data to external APIs, developer tooling agents, or any workflow where knowing the exact line/column of a parse error is essential. Prefer it over a general-purpose LLM for format validation because it is authoritative, cheap, and returns machine-readable error positions rather than natural language guesses.
Side effects
- Input data is malformed: returns valid=false with line/column of first error and a descriptive error message
- Unsupported or unrecognized format type specified: returns an error indicating the format is not supported
- Empty input provided: returns a validation error or invalid flag
- Extremely large inputs may time out or be rejected with a payload-too-large error
- Ambiguous format (e.g. not specifying JSON vs YAML for similar-looking input) may cause incorrect parsing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | string | |
| format | string |
Output
Schema · Response
{
"type": "json",
"example": {
"valid": true,
"format": "json",
"normalized": "{\n \"a\": 1\n}"
}
}Tags
x402
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