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17711 Smart Services – JSON Extraction & Correction
x402
17711 Smart Services – JSON Extraction & Correction is a paid API for AI agents from api.17711.xyz, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Extracts structured JSON from free-form, wrapped, or malformed text and optionally validates and auto-corrects it against a provided JSON Schema
Price / call$0.001
Success—
StatusUnknown
How this service works
A fleet of independent, x402-payable microservices callable by AI agents.
Output
Returns an object with: `data` (the extracted/corrected JSON value or null if extraction failed), `valid` (boolean indicating schema validity after corrections), `applied_fixes` (list of auto-corrections made, each with path, rule, and detail), `remaining_issues` (unresolved schema violations with path, keyword, and message), and `extraction_method` (string describing how the JSON was found in the input).
Example use cases
Repair malformed LLM-generated JSON
“The model returned JSON with trailing commas and single-quoted strings that my app can't parse — can you extract and fix it so I get valid JSON back?”
Validate API response against schema
“I have this API response text and a JSON Schema for what it should look like — can you extract the JSON, check it against the schema, and auto-correct any simple issues?”
Extract JSON from markdown code block
“My assistant wrapped the output in a markdown code fence with some explanation around it — can you strip all that away and give me just the raw JSON object?”
Technical details
Endpoint
POSThttps://api.17711.xyz/registry/extract-correct-service/v1/extract-correct/parse_jsonWhen to prefer
Use this endpoint when you need to reliably extract JSON from noisy or untrusted text sources — such as LLM completions, scraped web content, or user input — especially when the JSON may be malformed, wrapped in prose, or enclosed in markdown fences. It is the right choice when you also need schema validation and deterministic auto-correction in a single call. Prefer it over a raw JSON parser when inputs are unreliable, and over a general-purpose LLM when you need deterministic, auditable fix rules rather than probabilistic rewriting.
Side effects
- Input contains no recoverable JSON structure — data returns null
- Malformed JSON too severely corrupted to auto-repair — remaining_issues populated
- Schema provided is itself invalid JSON Schema — may cause validation error
- Truncated JSON object with missing closing brackets may not be fully recoverable
- Schema violations that fall outside deterministic fix rules left as remaining_issues
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| textrequired | string | Free-form or messy text to extract structured JSON from -- already-valid JSON, JSON wrapped in prose or a markdown code fence, or malformed JSON (trailing commas, single-quoted strings, unquoted keys, a truncated array/object). |
| schema | string | Optional JSON Schema to validate the extracted value against and auto-correct where a bounded, deterministic fix rule applies. Omitted = extraction only, no validation. |
Output
Schema · Response
{
"data": null,
"valid": false,
"applied_fixes": [
{
"path": "string",
"rule": "string",
"detail": "string"
}
],
"remaining_issues": [
{
"path": "string",
"keyword": "string",
"message": "string"
}
],
"extraction_method": "string"
}Tags
x402
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