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Deterministic A2A Broken-JSON Repair & Schema Validator
Deterministic A2A Broken-JSON Repair & Schema Validator is a paid API for AI agents from prompt-guard.agentdiet.dev, paid per call via MPP, $0.002/call, status unknown (last checked 2026-10-21).
Repairs truncated or malformed LLM JSON outputs (trailing commas, markdown fences, single quotes) so multi-agent pipelines never crash.
Price / call$0.002
Success—
StatusUnknown
How this service works
Repairs truncated or malformed LLM JSON outputs (trailing commas, markdown fences, single quotes) so multi-agent pipelines never crash. (Live x402 v2, IETF MPP & MCP endpoint at https://prompt-guard.agentdiet.dev/v1/json-normalizer)
Output
A repaired, valid JSON object reconstructed from the malformed input. The response contains the cleaned, schema-compliant JSON that can be safely parsed and passed to downstream pipeline steps without crashing.
Example use cases
LLM output normalization in agent pipeline
“The upstream agent just returned this JSON with trailing commas and single-quoted keys and my next step is failing — can you repair it so it's valid? Here's the raw string: {'name': 'Bob', 'items': ['a', 'b',]}”
Stripping markdown fences from model response
“My model wrapped its JSON in a markdown code block again and now my parser is throwing an error. Can you strip the fences and normalize it? The raw output is: ```json
{"result": "ok",}
```”
Fixing truncated JSON from streaming response
“I got a streaming response that got cut off mid-object and now the JSON is incomplete — something like {"user": "carol", "data": {"score". Can you repair it so it's parseable?”
Technical details
Endpoint
GEThttps://prompt-guard.agentdiet.dev/v1/json-normalizer?utm_source=zero.xyzWhen to prefer
Choose this endpoint when your multi-agent pipeline receives raw LLM-generated JSON that may contain markdown fences, trailing commas, single quotes, or truncation artifacts and you need a deterministic, low-latency fix before passing data downstream. It is especially valuable in agentic workflows where LLM output is unpredictable and pipeline stability is critical. Prefer it over writing custom regex-based fixups or retrying the LLM, as it provides consistent repair logic without additional token costs.
Side effects
- Input JSON too severely truncated to infer structure — may return partial or best-effort repair
- Ambiguous structure where multiple valid completions exist — repair may not match original intent
- Very large payloads may exceed query parameter length limits since input is passed via GET query string
- Non-JSON input that is fundamentally not JSON-like may return an error or empty result
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| raw_jsonrequired | string | Malformed, truncated, or markdown-wrapped JSON string to deterministically repair |
Output
Schema · Response
{
"type": "object"
}Reviews
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