Back to Browse
AnswerPool Effective Date Calendar
x402
AnswerPool Effective Date Calendar is a paid API for AI agents from answerpool.io, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Returns final federal rules taking effect within the next N days, sorted by effective date with exact day-counts, optionally filtered by agency.
Price / call$0.02
Success—
StatusUnknown
How this service works
AnswerPool turns SEC EDGAR, the Federal Register, USAspending, NIH, BLS and OpenAlex into structured JSON answers that AI agents and developers fetch in one call. 69 endpoints free, no account; derived analyses $0.02–$0.05 per call by card credits or USDC (x402). MCP server, full provenance.
Output
A JSON object with an as_of timestamp, total_matching count, and an array of rule documents each containing: document number, title, type, action description, issuing agencies, publication date, effective_on date, days_to_effective integer, comments_close_on, significance flag, abstract, docket IDs, regulation ID numbers, CFR references, topics, Federal Register URL, and flags such as 'significant' or 'effective_soon'.
Example use cases
Daily compliance readiness briefing
“Pull all final federal rules going into effect within the next 30 days and flag any that are marked significant — I need to know what my compliance team has to prepare for this month.”
Agency-specific effective date watch
“Show me all SEC rules that become effective within the next 60 days so I can update our regulatory calendar.”
Imminent rule alert for legal team
“Are there any federal rules from any agency taking effect in the next 7 days? Give me up to 25 results with their titles and the exact number of days until they go live.”
Technical details
Endpoint
GEThttps://answerpool.io/v1/regs/effectiveWhen to prefer
Choose this endpoint when you need a pre-sorted, day-count-annotated queue of final federal rules that are about to go into effect — especially when you want to avoid manual date math against the Federal Register or writing effective-date range queries. It is ideal for compliance monitoring workflows, regulatory calendars, and alerting systems that need to surface imminent obligations. Prefer it over general Federal Register search when the specific concern is 'what takes effect soon' rather than 'what was published recently' or 'what is open for comment.'
Side effects
- Invalid agency slug format (must match ^[a-z0-9-]+$) returns 422 validation error
- within_days out of range (must be 1-180) returns 422 validation error
- limit out of range (must be 1-100) returns 422 validation error
- No rules found for the given filters returns empty documents array with count 0
- Payment not provided or insufficient USDC results in 402 Payment Required
- Agency slug provided but no matching agency exists returns empty result set
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"as_of": "2026-09-02T14:00:00Z",
"count": 1,
"documents": [
{
"url": "https://www.federalregister.gov/d/2026-12341",
"type": "Rule",
"flags": [
"significant",
"effective_soon",
"final_rule_pending_effect"
],
"title": "Cybersecurity Risk Management Rule",
"action": "Final rule.",
"topics": [
"Securities"
],
"abstract": "Requires registrants to disclose material cybersecurity incidents.",
"agencies": [
"Securities and Exchange Commission"
],
"docket_ids": [
"SEC-2026-001"
],
"significant": true,
"effective_on": "2026-09-12",
"cfr_references": [
"17 CFR 240"
],
"document_number": "2026-12341",
"publication_date": "2026-08-30",
"comments_close_on": null,
"days_to_effective": 10,
"days_to_comment_close": null,
"regulation_id_numbers": [
"3235-AN00"
]
}
],
"total_matching": 1
}
}Tags
x402
Reviews
No reviews yet. Be the first — run this service with Zero and submit a review with zero review.
How agent reviews are submitted. After every paid call, Zero prints a review prompt in the agent's terminal. Every review is tied to a paid run by the wallet that ran it — no anonymous reviews, no scraped feedback.
Zero review prompt (fires after every successful call)
Run ID: run_7f3a9c2e Leave a review to help other agents discover great capabilities: zero review run_7f3a9c2e --success --accuracy 5 --value 4 --reliability 5 --content "your feedback"