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Payment Quotes — Missingness Co-occurrence
x402
Payment Quotes — Missingness Co-occurrence is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.0203/call, status unknown (last checked 2026-10-23).
Counts the joint absence (co-occurrence of nulls) of quote amount, network, and payee references across a batch of payment quotes.
Price / call$0.0203
Success—
StatusUnknown
How this service works
Count joint absence of quote amount, network and payee references. Canonical zero-unit amounts remain supplied metadata; absence is not a diagnosis of endpoint failure. Offline typed nonpersonal metadata only. Result retention: 24 hours; minimal receipt metadata: 90 days.
Output
Returns counts and patterns of jointly absent fields (quote amount, network, payee references) across the supplied payment quotes batch. Results are retained for 24 hours; minimal receipt metadata is kept for 90 days. Zero-unit amounts are treated as supplied metadata, not as missing values.
Example use cases
Pre-submission quote completeness audit
“I have a set of up to 30 payment quotes I'm about to process — can you check them for co-occurring missing fields like amount and network, so I can catch data quality issues before submission? Data class is public, all metadata is non-personal.”
Synthetic data pipeline quality check
“We generated a batch of synthetic payment quotes for testing — can you run a missingness co-occurrence check to see how many quotes are simultaneously missing payee, amount, or network references?”
Recurring payment metadata gap detection
“Here are 20 payment quotes from our records. Can you count how often amount and network fields are both absent at the same time, so I know where the joint data gaps are? Treat this as public, non-personal metadata.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/verify/payment-quotes/missingness-cooccurrence?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to quantify how often multiple payment quote fields are simultaneously absent — not just individually missing. Ideal for data quality pipelines that want to detect structural gaps in quote metadata (amount, network, payee) across a batch, especially when working with synthetic or public non-personal data. Prefer this over single-field coverage checks when joint absence patterns matter for downstream processing decisions.
Side effects
- Array of quotes is empty or exceeds 32 items — validation error
- dataClass not set to 'public' or 'synthetic' — enum validation failure
- nonpersonalMetadata not set to true — rejected by const constraint
- Malformed quote objects in the array — schema validation error
- Network timeout or service unavailability — HTTP 5xx response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| quotes | array | |
| dataClass | string | |
| nonpersonalMetadata | boolean |
Tags
x402
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