Know who you're paying before your agent pays them
Before your agent pays, trusts or recommends a company or a token, it can buy a deep dive: primary SEC filings, a cited research brief, current sentiment and a red-flag pre-screen. That's $1.28 for a public company or $1.33 for a token, paid per call in USDC over x402, with no account. Here's a real run on Coinbase, Circle and Chainlink from 2026-10-01.
17/17deliverables passed the quality check
17/17receipts verified
4/4runs anchored on Base mainnet
22smedian time per deliverable (max 33s)
Primary data, not a summary of it. The SEC EDGAR SKU returns the filings themselves (10-K, 10-Q, Form 4) with links to the documents.
Every claim cites a source. Research briefs carry inline [n] citations and a source list your agent can follow.
Checked before you get it. Each deliverable is screened for refusals, empty lookups, off-topic sources and claims contradicted by SEC records.
Provable after you get it. Each deliverable gets a content hash and a receipt, and every run's Merkle root is written to Base.
Buy it
# 1. ask for the price: the server answers 402 with an x402 challenge
curl -s -X POST https://api.craigmbrown.com/v1/services/data.sec-edgar-filing \
-H 'content-type: application/json' -d '{"ticker":"COIN","form":"10-Q","count":3}'
# 2. sign an EIP-3009 USDC authorization for the quoted amount (Base)
# 3. resend the same request with header X-PAYMENT: <base64 signed authorization>
# -> deliverable + trust envelope (content_sha256, scan verdict, provenance)
Discovery for agents: /v1/services (live catalog with input schemas) ·
agent-services.json · /v1/rules (fees and rules) ·
llms.txt. The same SKUs are callable as MCP tools at https://api.craigmbrown.com/v1/mcp.
Machine-readable offer
The same offer as JSON, embedded in this page as <script type="application/ld+json" id="blindoracle-offer">.
{
"@context": "https://schema.org",
"@type": "Offer",
"name": "BlindOracle entity deep dive",
"seller": {
"@type": "Organization",
"name": "BlindOracle",
"url": "https://craigmbrown.com/blindoracle/"
},
"description": "Before an agent pays, trusts or recommends a company or token: primary SEC data, cited research, sentiment and a red-flag pre-screen, each deliverable quality-checked, hashed, receipted and anchored on Base.",
"priceSpecification": [
{
"name": "equity bundle",
"price": 1.28,
"priceCurrency": "USD"
},
{
"name": "token bundle",
"price": 1.33,
"priceCurrency": "USD"
}
],
"payment": {
"protocol": "x402",
"asset": "USDC",
"network": "base",
"flow": "POST sku url -> 402 challenge -> sign EIP-3009 -> resend with X-PAYMENT"
},
"skus": {
"equity": [
"https://api.craigmbrown.com/v1/services/data.sec-edgar-filing",
"https://api.craigmbrown.com/v1/services/data.sec-edgar-filing",
"https://api.craigmbrown.com/v1/services/data.business-registry",
"https://api.craigmbrown.com/v1/services/ops.due-diligence-scan",
"https://api.craigmbrown.com/v1/services/research.topic-deep-researcher",
"https://api.craigmbrown.com/v1/services/research.topic-sentiment-analyzer"
],
"token": [
"https://api.craigmbrown.com/v1/services/crypto.market-analyzer",
"https://api.craigmbrown.com/v1/services/oracle.comprehensive-report",
"https://api.craigmbrown.com/v1/services/oracle.sentiment-analysis",
"https://api.craigmbrown.com/v1/services/research.topic-deep-researcher",
"https://api.craigmbrown.com/v1/services/ops.due-diligence-scan"
]
},
"discovery": [
"https://api.craigmbrown.com/v1/services",
"https://craigmbrown.com/.well-known/agent-services.json",
"https://api.craigmbrown.com/v1/rules",
"https://craigmbrown.com/llms.txt"
],
"example_run": {
"date": "2026-10-01",
"entities": [
"COIN",
"CRCL",
"LINK"
],
"deliverables": 17,
"passed_quality_check": 17,
"receipts_verified": 17,
"base_anchor_txs": [
"https://basescan.org/tx/0x096c8a27ffb5b082842f15bd88a38d433f78dd771409b2d4839e7fb46be0402b",
"https://basescan.org/tx/0x04cd0dd8fddc474b8ccd6f1b104d6b3ae892fc7615650a29ebf847befe114cc2",
"https://basescan.org/tx/0xc0d1d7ce1b4bce260c85242ab7fabd6aff1b5d1ac14599edc735000b7ef2d4b8",
"https://basescan.org/tx/0x9b4d5c6ac06089531aaf59d94ab252b51024698ff56868e0035f965a8a9aa63d"
]
}
}
Research and data for due diligence, not financial advice. LLM deliverables are AI-generated and labelled as such in their trust envelope.