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FinOps Token Spend Audit
x402
FinOps Token Spend Audit is a paid API for AI agents from api.craigmbrown.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-15).
Audits an AI agent's actual token usage and cost against its declared budget and task scope, surfacing overruns and scope creep.
Price / call$0.05
Success—
StatusUnknown
How this service works
Independent audit of an agent's actual token spend against its budget and declared task scope — surfaces cost overruns or scope creep a counterparty wouldn't otherwise see.
Output
Returns a JSON object with the overall audit status, cost totals for the period, spending growth metrics, an array of specific findings (overruns, anomalies, scope creep events), projected monthly savings in USD, a trust score from BlindOracle, underlying assumptions, and a verify_recipe string so the counterparty can independently confirm the audit.
Example use cases
Monthly budget compliance check
“Audit the past 30 days of token usage for my 'data-pipeline-agent' using Claude Sonnet — I set a $200/month budget and want to know if it stayed within bounds or if there are any overruns I should be aware of.”
Detecting unexpected agent scope creep
“Something feels off with my agent fleet — can you run a token spend audit over the last 7 days across all agents and tell me if any of them are doing significantly more work than their declared task scope would justify?”
Projecting cost savings before contract renewal
“I want to renegotiate my LLM provider contract — can you audit 90 days of token spend records across our agents and give me a projected monthly savings figure if we cut the identified inefficiencies?”
Technical details
Endpoint
POSThttps://api.craigmbrown.com/v1/services/finops.token-spend-auditWhen to prefer
Choose this endpoint when you need an independent, verifiable audit of an agent's token expenditure that a counterparty can trust — particularly for accountability between agent operators and clients, compliance checks against declared budgets, or detecting cost growth that internal dashboards might obscure. It is purpose-built for FinOps on LLM workloads rather than general-purpose analytics.
Side effects
- Empty or malformed records array yields no findings but may return a vacuous pass status
- Timestamps outside the declared window_days range may be silently excluded from totals
- Missing price_book causes cost calculations to fall back to defaults that may not match actual provider pricing
- Records exceeding the 10,000 item limit are rejected with a validation error
- Incomplete agent or model fields reduce attribution accuracy in per-agent breakdowns
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| price_book | object | |
| thresholds | object | |
| window_days | number |
Output
Schema · Response
{
"type": "json",
"schema": {
"type": "object",
"properties": {
"growth": {
"type": "object"
},
"period": {
"type": "object"
},
"status": {
"type": "string"
},
"totals": {
"type": "object"
},
"asserts": {
"type": "string"
},
"bo_trust": {
"type": "object"
},
"findings": {
"type": "array",
"items": {
"type": "object"
}
},
"assumptions": {
"type": "array",
"items": {
"type": "string"
}
},
"verify_recipe": {
"type": "string"
},
"deliverable_type": {
"type": "string"
},
"projected_monthly_saving_usd": {
"type": "number"
}
}
},
"example": {
"deliverable_type": "..."
}
}Tags
x402
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