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netintel.dev Schema Mapper
x402
netintel.dev Schema Mapper is a paid API for AI agents from netintel.dev, paid per call via x402, $0.04/call, status unknown (last checked 2026-10-13).
Transforms a source object into a target schema by matching fields via aliases, structure, and optional LLM-based semantic mapping, with per-field confidence scores and a detailed mapping report.
Price / call$0.04
Success—
StatusUnknown
How this service works
Transform a source object into a target schema — matches fields by alias and structure deterministically, uses an LLM only for semantic mapping when needed, and supports mapping hints, transforms, and defaults, so agents can reshape data between systems with per-field confidence and a clear report of what mapped and what didn't.
Output
Returns a mapped object conforming to the target schema, with per-field confidence scores, a letter grade for overall mapping quality, and lists of successfully mapped fields, missing target fields, and unmapped source fields. In permissive mode, unmatched required fields are null with warnings; in strict mode they are omitted.
Example use cases
CRM data ingestion normalization
“I'm pulling records from an external CRM that uses 'first_name', 'last_name', and 'phone_number', but my system expects 'full_name' and 'phone' — can you map the source object to my target schema, merge the name fields semantically, and flag anything that didn't map?”
E-commerce order pipeline bridging
“I have a raw order object from our fulfillment partner with fields like 'amt', 'qty', and 'ship_to', and I need it reshaped to match our internal schema that expects 'amount' as a number, 'quantity' as an integer, and 'shipping_address' as a string — run the mapping with toNumber and toInteger transforms on the numeric fields.”
Strict schema validation for data pipeline
“Before we insert this event payload into our database, map it strictly against our required schema — 'event_type' (string), 'timestamp' (ISO string), and 'user_id' (string) — with allow_llm set to false so only deterministic field matching is used, and tell me exactly which required fields are missing.”
Technical details
Endpoint
POSThttps://netintel.dev/schema/map?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to reshape a JSON object between two schemas with different field names, structures, or types — especially when field names are semantically similar but not identical (e.g. 'name' vs 'full_name'), or when you need deterministic alias matching plus optional LLM fallback for ambiguous fields. It's ideal for integration pipelines, ETL workflows, and API bridging where you need a structured report of what mapped and what didn't, with per-field confidence. Prefer this over generic LLM prompting when you want deterministic alias resolution first and LLM only as a fallback, and when you need explicit transforms (toNumber, toISO, etc.) and defaults applied automatically.
Side effects
- Source object exceeds 64KB or 200 fields — rejected
- Target schema is malformed or unparseable — error returned
- allow_llm=false and fields can't be deterministically matched — fields land in missing_target
- Hints reference non-existent source fields — mapping falls back to automatic
- Invalid transform name specified — per-field error in report
- Ambiguous semantic mapping with low confidence — reflected in per-field confidence scores
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"grade": "A",
"score": 100,
"mapped": {
"price": 19.99,
"full_name": "Jane Doe"
},
"findings": [],
"warnings": [],
"schema_valid": true,
"mapped_fields": [
{
"method": "alias",
"source": "name",
"target": "full_name",
"confidence": 1
},
{
"method": "alias",
"source": "cost",
"target": "price",
"confidence": 1
}
],
"missing_target": [],
"unmapped_source": [
"internal_id"
],
"type_conversions": [
{
"to": "number",
"from": "string",
"field": "price"
}
]
}
}Tags
x402
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