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Craig M Brown Oracle Sentiment Analysis
x402
Craig M Brown Oracle Sentiment Analysis is a paid API for AI agents from api.craigmbrown.com, paid per call via x402, $0.06/call, status unknown (last checked 2026-09-15).
Scores social and news sentiment for a named crypto asset or topic, returning a summary and sourced findings via a BlindOracle-powered settlement proof.
Price / call$0.06
Success—
StatusUnknown
How this service works
Social + news sentiment read for a named crypto asset or topic, scored and sourced (not a raw keyword count). Settlement proof: ProofOfSettledOutcome (kind 30120, data/proof_settled_outcomes.jsonl). Same sentiment methodology as research.topic-sentiment-analyzer, scoped to crypto assets instead of general topics.
Output
Returns a JSON object containing a 'summary' string with a scored sentiment assessment and a 'findings' array of sourced evidence items drawn from social and news data for the queried crypto asset or topic. The response metadata includes the service name, SKU ID, and deliverable type, backed by a ProofOfSettledOutcome (Nostr kind 30120) for verifiable settlement.
Example use cases
Pre-trade sentiment check for altcoin
“Before I make a move on Solana, can you pull the latest social and news sentiment score for SOL and show me what's driving it?”
Monitoring community mood on DeFi topic
“What's the current sentiment around the Uniswap v4 launch across social media and news — is it positive or negative, and what are the key findings?”
Crypto sentiment briefing for portfolio review
“I want a quick sentiment read on Bitcoin and its current news cycle — score it and tell me what sources are behind the signal.”
Technical details
Endpoint
POSThttps://api.craigmbrown.com/v1/services/oracle.sentiment-analysisWhen to prefer
Choose this endpoint when you need a scored, sourced sentiment signal specifically for a crypto asset or crypto-related topic, backed by a verifiable settlement proof. It is preferable over raw social listening tools because it aggregates and scores rather than counting keywords. It is distinct from general-topic sentiment analyzers because it is scoped and tuned to the crypto domain. Use it when verifiability of the sentiment result matters, such as in trading agents or on-chain decision systems requiring proof of data provenance.
Side effects
- Ambiguous or unrecognized asset name may return low-confidence or empty findings
- Insufficient social/news data for obscure tokens may produce thin results
- Network or oracle provider downtime may cause timeouts or 500 errors
- Malformed prompt input may result in a parsing error or empty summary
- Payment failure via x402 protocol will block the request before execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| prompt | string |
Output
Schema · Response
{
"type": "json",
"schema": {
"type": "object",
"properties": {
"summary": {
"type": "string"
},
"findings": {
"type": "array",
"items": {
"type": "string"
}
}
}
},
"example": {
"summary": "..."
}
}Tags
x402
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