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NetIntel Schema Map
x402
NetIntel Schema Map is a paid API for AI agents from netintel-production-440c.up.railway.app, paid per call via x402, $0.04/call, status unknown (last checked 2026-09-14).
Transforms a source JSON object into a target schema shape using rule-based and semantic field mapping with type coercions, hints, and defaults
Price / call$0.04
Success—
StatusUnknown
How this service works
Network intelligence API — 138 endpoints, all pay-per-call via x402 micropayments (USDC on Base or Solana mainnet). NetIntel is agent fair-trade aligned: transparent per-call pricing in USDC on Base or Solana via x402, no API keys or signup, and automatic no-charge on server errors, upstream failures, and input-validation rejections — failed calls are never billed across NetIntel's network, domain, and data-intelligence endpoints.
Output
Returns a mapped object conforming to the target schema, a letter grade (A–F) and numeric score reflecting mapping quality, lists of missing target fields, unmapped source fields, type conversions performed, per-field mapping method and confidence, schema validity flag, and any warnings about partial or ambiguous matches.
Example use cases
Normalize webhook payload to database schema
“I'm getting webhook payloads from Stripe with fields like 'amount_total', 'customer_email', and 'created' but my database expects 'total', 'email', and 'created_at' as an ISO date string — can you map this payload across and coerce the types to match my schema?”
Harmonize data from two vendor APIs
“I have product records from two different supplier APIs with completely different field names and I need them both normalized to a common schema with fields: product_name (string), sku (string), price (number), and in_stock (boolean) — map each record over and tell me what fields couldn't be matched.”
Validate and reshape form submission data
“I've got a raw form submission object and a strict target schema for my CRM — map the form fields to the CRM schema, apply trim and toLowerCase transforms where needed, use 'USD' as the default currency if it's missing, and flag any required fields that couldn't be filled.”
Technical details
Endpoint
POSThttps://netintel-production-440c.up.railway.app/schema/mapWhen to prefer
Choose this endpoint when you need to programmatically reshape or normalize a JSON object from one field-naming convention or type structure to another — especially when field names differ semantically (not just syntactically) and you want LLM-assisted matching as a fallback. It is particularly useful for ETL pipelines, API integration glue code, webhook normalization, and CRM data onboarding where manual mapping would be tedious. Prefer it over simple find-and-replace or static mapping when field relationships are ambiguous, types need coercion, or you need an audit trail of what mapped to what and how confidently.
Side effects
- Source object exceeds 64KB or 200 fields — rejected with validation error, no charge
- Target schema is malformed or unparsable — input validation rejection, no charge
- Required fields missing from input body — 400-level validation error, no charge
- Server or upstream failure during semantic mapping — automatic no-charge per NetIntel policy
- Strict mode with unmapped fields — returns 200 with missing_target populated rather than an error
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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