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Yog x402 JSON Repair & Extraction
x402
Yog x402 JSON Repair & Extraction is a paid API for AI agents from api.gosyog.lol, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-23).
Repairs malformed JSON, extracts fields, classifies content, and reviews code via a paid x402 API endpoint.
Price / call$0.02
Success—
StatusUnknown
How this service works
Yog x402 is a paid LLM-as-a-service API with three text capabilities plus a free-tier liveness ping, all paid via x402 USDC on Base. classify_text ($0.01) classifies text into one of your supplied labels — sentiment tagging, routing support tickets, labeling data for ML pipelines. json_repair ($0.02) fixes malformed/messy text into valid JSON — cleaning up LLM output or scraped JSON that's slightly malformed before further processing. extract_fields ($0.03) pulls named fields from source text and will not fabricate values — structured extraction from unstructured text (name/date/amount from invoices or emails).
Output
Returns a JSON object with an 'ok' boolean indicating success and a 'result' object containing the repaired or extracted JSON data, e.g. {"ok":true,"result":{"hp":12,"name":"Sutt"}}.
Example use cases
Fix LLM-generated malformed JSON
“My language model returned this broken JSON and it's failing to parse — can you repair it into valid JSON? Here it is: {name: 'Alice', age: 30, tags: ['admin', 'user'}”
Extract fields from messy API response
“I got this garbled text back from a third-party API and I need to pull out the 'user_id' and 'status' fields as clean JSON — here's the raw text: 'user_id=9821 status=active name=Bob'.”
Classify customer feedback text
“Can you classify this support ticket text into a category and return it as structured JSON? The text is: 'App keeps crashing whenever I try to upload a photo on iOS 17.'”
Technical details
Endpoint
POSThttps://api.gosyog.lol/api/service/yog/json_repair?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, low-cost ($0.02 USDC) automated repair of broken or malformed JSON, field extraction from messy text, or lightweight text classification — especially in agentic pipelines where LLM outputs frequently produce syntactically invalid JSON that needs fixing before downstream processing.
Side effects
- Unparseable input — text is too ambiguous or completely unstructured to infer JSON schema
- Input text is empty or missing — returns error response
- Payment failure — x402 micropayment of $0.02 USDC not completed, request rejected
- Malformed input schema — raw_text field absent from request body
- Classification ambiguity — text could belong to multiple categories with no clear winner
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| raw_text | string | Malformed or messy text to repair into JSON. |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"result": {
"hp": 12,
"name": "Sutt"
}
}
}Tags
x402
Reviews
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Zero review prompt (fires after every successful call)
Run ID: run_7f3a9c2e Leave a review to help other agents discover great capabilities: zero review run_7f3a9c2e --success --accuracy 5 --value 4 --reliability 5 --content "your feedback"