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Study Notes Scope Boundary Audit
x402
Study Notes Scope Boundary Audit is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.038296/call, status unknown (last checked 2026-10-16).
Filters study note metadata by exact notebook ID and allowed claim-kind declarations, validating scope boundaries without inferring truth or learner profiles.
Price / call$0.038296
Success—
StatusUnknown
How this service works
Filter note metadata by exact notebook and allowed claim-kind declarations before selection. No inferred truth, learner profile or claim completeness. Offline typed nonpersonal metadata only. Result retention: 24 hours; minimal receipt metadata: 90 days.
Output
Returns filtered note metadata that passes the scope boundary check — only notes within the specified notebookId and matching one of the allowedKinds claim types are included. Result includes a minimal receipt metadata record retained for 90 days; full result retained for 24 hours. No learner profile data, inferred truth, or claim completeness information is returned.
Example use cases
Notebook claim-kind allowlist enforcement
“I need to audit my study notes in notebook 'bio-101-spring' and keep only those with allowed kinds ['fact','hypothesis'] from the public data class — give me just the metadata scope check, nothing inferred.”
Synthetic note metadata boundary check
“Run a scope boundary audit on these 15 study notes against notebookId 'chemistry-lab-2024', using the synthetic data class and allowing only claim kinds ['observation','measurement'].”
Pre-export note metadata validation
“Before I export, can you filter these study notes from notebook 'history-research' to confirm they all fall within claim kinds ['citation','summary'] and are classified as public nonpersonal metadata?”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/context/study-notes/scope-boundary-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to filter or audit study note metadata strictly by notebook scope and declared claim-kind allowlists, without any AI inference, learner profiling, or claim completeness checking. It is ideal for offline, privacy-preserving metadata validation workflows where only typed nonpersonal metadata is acceptable and results are needed quickly with short retention.
Side effects
- Missing or invalid notebookId causes rejection of the entire batch
- Notes array exceeding 32 items returns a validation error
- allowedKinds array with more than 3 entries is rejected
- dataClass value outside ['public','synthetic'] enum causes error
- nonpersonalMetadata not set to true (const) causes schema validation failure
- Empty notes array (fewer than 1 item) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| notes | array | |
| dataClass | string | |
| notebookId | string | |
| allowedKinds | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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