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Signal Ledger Research API – Coverage Check
x402
Signal Ledger Research API – Coverage Check is a paid API for AI agents from 43-135-4-66.sslip.io, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).
Checks a submitted financial report (10-K, 10-Q, annual/quarterly filing) for risk signals and returns evidence-linked risk scores, severity assessments, and follow-up recommendations.
Price / call$0.005
Success—
StatusUnknown
How this service works
Financial statement coverage check / 财报可分析性检查. Verify whether supplied filing text contains the statements, period, metric basis and original source needed for reliable analysis before buying a full audit. Deterministic; no AI.
Output
Returns a JSON object with a coverage score (0-100), readiness flag, risk level, severity, confidence rating, data freshness status (age in days), evidence quotes and trace, list of missing information, schema version, document hash, and recommended follow-up actions. If no risks are detected, the risks array is empty.
Example use cases
10-K risk screening before investment
“I'm considering investing in Tesla. Can you run a risk check on this Tesla FY2024 10-K text I have and tell me the coverage score, risk level, and any evidence-linked red flags I should know about?”
Quarterly earnings red flag detection
“Here's the plain text of Alibaba's latest 10-Q from their investor relations page — can you check it for financial risks and tell me how confident the assessment is and what follow-up review is recommended?”
Filing freshness and completeness audit
“I need to know if this annual report I pulled from the HKEX website is fresh enough to use for analysis and whether it has sufficient coverage — here's the full report text, it was published on 2025-03-15 and covers FY2024.”
Technical details
Endpoint
POSThttps://43-135-4-66.sslip.io/v1/filings/coverage-checkWhen to prefer
Choose this endpoint when you need evidence-linked, traceable risk assessment of a specific financial filing text (10-K, 10-Q, annual/quarterly report) with provenance metadata support. It is purpose-built for AI agent workflows that require structured risk signals with citations rather than generic summarization. Prefer it over general LLM summarization when you need a consistent risk score schema, freshness checks, and audit-ready evidence traces.
Side effects
- Report text shorter than 200 characters returns a validation error
- Invalid or missing reportText field causes a 400 error
- sourceUrl not matching https?:// pattern fails schema validation
- Report text exceeding 100,000 characters is rejected
- Stale or unrecognized report period may lower confidence to 'low'
- Network timeout if report text is very large
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| company | string | Optional company name |
| reportText | string | Plain-text 10-K, 10-Q, annual report, quarterly report or financial statement supplied by the caller |
| sourceContext | object | Optional provenance metadata for report-period, freshness, metric-basis and source traceability checks |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"risks": [],
"product": {
"id": "coverage",
"price": "$0.005",
"uses_ai": false
},
"coverage": {
"score": 78,
"ready_for_analysis": true
},
"severity": "not_assessed",
"confidence": "medium",
"risk_level": "not_assessed",
"risk_score": null,
"source_url": "https://www.sec.gov/Archives/edgar/data/000000/example.htm",
"report_period": "FY2025",
"risk_category": "not_assessed",
"data_freshness": {
"status": "fresh",
"age_days": 2
},
"evidence_quote": null,
"evidence_trace": [],
"schema_version": "2026-09-10",
"document_sha256": null,
"source_location": null,
"missing_information": [],
"evidence_verification": "not_applicable",
"recommended_follow_up": [
"Review the cash-flow statement and working-capital footnotes."
]
}
}Tags
x402
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